From 4691e72e2cb161928377e6c6665b7f7fdcec6bc5 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 11:06:15 -0400
Subject: [PATCH 01/19] Add support for recording last time tested, as well as
 giving priority in random testing to people for which more time passed before
 they were last tested

---
 seirsplus/models.py    |  3 +++
 seirsplus/sim_loops.py | 19 +++++++++++++++----
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/seirsplus/models.py b/seirsplus/models.py
index c4ddd40..127abac 100644
--- a/seirsplus/models.py
+++ b/seirsplus/models.py
@@ -1880,6 +1880,7 @@ def __init__(self, G, beta, sigma, lamda, gamma,
         # Initialize other node metadata:
         #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         self.tested      = numpy.array([False]*self.numNodes).reshape((self.numNodes,1))
+        self.testedTime = numpy.array([-1] * self.numNodes).reshape((self.numNodes, 1)) # the time that the node was last tested: negative means it was not tested
         self.positive    = numpy.array([False]*self.numNodes).reshape((self.numNodes,1))
         self.numTested   = numpy.zeros(6*self.numNodes) 
         self.numPositive = numpy.zeros(6*self.numNodes) 
@@ -2470,6 +2471,8 @@ def set_isolation(self, node, isolate):
 
     def set_tested(self, node, tested):
         self.tested[node] = tested
+        if tested:
+            self.testedTime[node] = self.t # set time that the node was tested to current time
         self.testedInCurrentState[node] = tested
 
 #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 1202512..339aa5d 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -3,7 +3,7 @@
 import numpy
 
 import time
-
+import random
 
 
 def run_tti_sim(model, T, 
@@ -17,7 +17,11 @@ def run_tti_sim(model, T,
                 isolation_compliance_positive_individual=[None], isolation_compliance_positive_groupmate=[None],
                 isolation_compliance_positive_contact=[None], isolation_compliance_positive_contactgroupmate=[None],
                 isolation_lag_symptomatic=1, isolation_lag_positive=1, isolation_lag_contact=0, isolation_groups=None,
-                cadence_testing_days=None, cadence_cycle_length=28, temporal_falseneg_rates=None
+                cadence_testing_days=None, cadence_cycle_length=28, temporal_falseneg_rates=None,
+                test_priority = 'random'
+                # test_priority: how to to choose which nodes to test:
+                # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
+                # A suffix of "degree_oblivious" means that we ignore degrees (i.e., assume we don't know social networks for testing policy)
                 ):
 
     #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -255,10 +259,17 @@ def run_tti_sim(model, T,
                         numRandomTests = max(min(tests_per_day-len(tracingSelection)-len(symptomaticSelection), len(testingPool)), 0)
                         
                         testingPool_degrees       = model.degree.flatten()[testingPool]
-                        testingPool_degreeWeights = numpy.power(testingPool_degrees,random_testing_degree_bias)/numpy.sum(numpy.power(testingPool_degrees,random_testing_degree_bias))
+                        if "degree_oblivious" in test_priority:
+                            testingPool_degreeWeights = numpy.ones(len(testingPool))
+                        else:
+                            testingPool_degreeWeights = numpy.power(testingPool_degrees,random_testing_degree_bias)/numpy.sum(numpy.power(testingPool_degrees,random_testing_degree_bias))
 
                         if(len(testingPool) > 0):
-                            randomSelection = testingPool[numpy.random.choice(len(testingPool), numRandomTests, p=testingPool_degreeWeights, replace=False)]
+                            if 'last_tested' in test_priority:
+                                # sort the pool according to the time they were last tested, breaking ties randomly
+                                randomSelection = sort(testingPool,key = lambda i: model.testedTime[i], cmp = lambda x,y: x-y if x-y else random.randint(0, 1) * 2 - 1  )[:numRandomTests]
+                            else:
+                                randomSelection = testingPool[numpy.random.choice(len(testingPool), numRandomTests, p=testingPool_degreeWeights, replace=False)]
 
                 
                 #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From 4159e1bc69425f1f74698a1be3e018d2c88fd700 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 11:12:20 -0400
Subject: [PATCH 02/19] Notebook with priority testing

---
 ...EIRS_Workplace_TTI_Demo_timePriority.ipynb | 853 ++++++++++++++++++
 1 file changed, 853 insertions(+)
 create mode 100644 examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb

diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
new file mode 100644
index 0000000..45a2eec
--- /dev/null
+++ b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
@@ -0,0 +1,853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extended SEIRS Workplace TTI Demo\n",
+    "\n",
+    "**In this demonstration we will explore the effect of testing, tracing, and isolation interventions on disease transmission in a workplace setting with a realistic contact network.**\n",
+    "\n",
+    "This notebook provides a demonstration of the functionality of the [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description) and the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). This notebook also offers a sandbox for starting to explore TTI scenarios of your own. \n",
+    "For a more thorough walkthrough of the model, simulation loop, and use of this package, refer to the [SEIRS+ Wiki](https://github.com/ryansmcgee/seirsplus/wiki)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installing and importing the model code\n",
+    "\n",
+    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
+    "\n",
+    "In this demo we will also use features from the `networks`, `sim_loops`, and `utilities` modules.\n",
+    "\n",
+    "#### Install the package using ```pip```\n",
+    "The package can be installed on your machine by entering this in the command line:\n",
+    "\n",
+    "```pip install seirsplus```\n",
+    "\n",
+    "Then, the ```models```, `networks`, `sim_loops`, and `utilities` modules can be imported as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seirsplus.models import *\n",
+    "from seirsplus.networks import *\n",
+    "from seirsplus.sim_loops import *\n",
+    "from seirsplus.utilities import *\n",
+    "import networkx\n",
+    "import matplotlib.pyplot as pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *Alternatively, manually copy the code to your machine*\n",
+    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
+    "```python\n",
+    "from models import *\n",
+    "from networks import *\n",
+    "from sim_loops import *\n",
+    "from utilities import *\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set basic parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the workplace size and structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NUM_COHORTS              = 4\n",
+    "NUM_NODES_PER_COHORT     = 200\n",
+    "NUM_TEAMS_PER_COHORT     = 10\n",
+    "\n",
+    "MEAN_INTRACOHORT_DEGREE  = 6\n",
+    "PCT_CONTACTS_INTERCOHORT = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = NUM_NODES_PER_COHORT*NUM_COHORTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we set the initial prevalence to be a single case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INIT_EXPOSED = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------\n",
+    "\n",
+    "## Specifying contact networks\n",
+    "\n",
+    "This package implements models epidemic dynamics for populations with a structured [contact network](Extended-SEIRS-Model-Description#contact-networks). Individuals are represented as nodes in a network, and parameters, contacts, and interventions can be specified on a targeted individual basis. A graph specifying the contact network must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
+    "\n",
+    "This model also supports scenarios where individuals enter quarantine states in which their parameters and interactions may be different from baseline, and a separate graph defining the interactions for individuals in quarantine can be specified (i.e., the [quarantine contact network](Extended-SEIRS-Model-Description#quarantine-contacts)).\n",
+    "\n",
+    "### Workplace Contact Network\n",
+    "\n",
+    "Here we use the [**demographic community network generator**](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#workplace-network) defined in the SEIRS+ package. This function generates a contact network that resembles workplaces and other multi-level modular populations.\n",
+    "\n",
+    "[FARZ](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#farz-networks) network layers are generated to represent cohorts of employees (e.g., departments, floors, shifts). FARZ networks have a tunable community structure, so each cohort includes some number of communities, which can be thought to represent teams (i.e., groups of employees that work closely with each other). Employees may belong to more than one team (specified by a FARZ parameter), but employees belong to only one cohort. An employee's intra-team and intra-cohort contacts are defined by the FARZ cohort network they belong to. A specified percentage of each employee's total number of workplace contacts can be with individuals from other cohorts. An employee's inter-cohort contacts are drawn randomly from the pool of individuals outside their own cohort. \n",
+    "\n",
+    "The number of cohorts, number of employees per cohort, number of teams per cohort, number of teams employees belong to, mean intra-cohort degree, percent of within- and between-team connections, and percent of intra- and inter-cohort connections can be controlled with the arguments to the `generate_demographic_contact_network()` function (some of which are passed as [parameters to the FARZ generator](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#FARZ-parameters)).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline:\n",
+      "Degree: mean = 11.11, std = 8.41, 95% CI = (1.00, 29.00)\n",
+      "        coeff var = 0.76\n",
+      "Assortativity:    0.28\n",
+      "Clustering coeff: 0.24\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G_baseline, cohorts, teams = generate_workplace_contact_network(\n",
+    "                                 num_cohorts=NUM_COHORTS, num_nodes_per_cohort=NUM_NODES_PER_COHORT, \n",
+    "                                 num_teams_per_cohort=NUM_TEAMS_PER_COHORT,\n",
+    "                                 mean_intracohort_degree=MEAN_INTRACOHORT_DEGREE, \n",
+    "                                 pct_contacts_intercohort=PCT_CONTACTS_INTERCOHORT,\n",
+    "                                 farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10, \n",
+    "                                              'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False})\n",
+    "\n",
+    "network_info(G_baseline, \"Baseline\", plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define the quarantine contact network to be an empty network (i.e., no connections). This represents an assumption that an employee that is in a quarantine state makes no contact with anyone from their workplace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_quarantine = networkx.classes.function.create_empty_copy(G_baseline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specifying parameters\n",
+    "\n",
+    "**_The parameter values used in this notebook reflect rough estimates of parameter values for the COVID-19 epidemic (as of 9 Aug 2020)._**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set disease progression rate parameters:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parameter values are assigned to members of the population on an individual basis. Parameter values can be [specified to the `ExtSEIRSNetworkModel`](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#specifying-parameters) by providing a list of values that gives the *N* values to assign to each individual. The population may be either homogeneous or heterogeneous for a given parameter at the user's discretion. \n",
+    "\n",
+    "**Here we generate distributions of values for each parameter, thus specifying a realistically heterogeneous population.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected latent periods (time in Exposed state) and presymptomatic periods (time in Pre-symptomatic infectious state). The `sigma` and `lamda` rates are calculated as the inverse of the expected exposed and pre-symptomatic periods assigned to each individual, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "latent period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
+      "\n",
+      "pre-symptomatic period:  mean = 2.94, std = 1.72, 95% CI = (0.64, 7.12)\n",
+      "\n",
+      "total incubation period:  mean = 5.14, std = 2.03, 95% CI = (2.17, 10.02)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latentPeriod_mean, latentPeriod_coeffvar = 3.0, 0.6\n",
+    "SIGMA   = 1 / gamma_dist(latentPeriod_mean, latentPeriod_coeffvar, N)\n",
+    "\n",
+    "presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar = 2.2, 0.5\n",
+    "LAMDA   = 1 / gamma_dist(presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/LAMDA, 1/SIGMA, 1/LAMDA+1/SIGMA], [\"latent period\", \"pre-symptomatic period\", \"total incubation period\"], plot=True, colors=['gold', 'darkorange', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected (a)symptomatic periods (time in symptomatic or asymptomatic state). The `gamma` rates are calculated as the inverse of the expected (a)symptomatic periods assigned to each individual. \n",
+    "\n",
+    "The expected total infectious period for each individual is the sum of their expected pre-symptomatic and (a)symptomatic periods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pre-symptomatic period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
+      "\n",
+      "(a)symptomatic period:  mean = 4.01, std = 1.54, 95% CI = (1.64, 7.36)\n",
+      "\n",
+      "total infectious period:  mean = 6.20, std = 1.87, 95% CI = (3.09, 10.45)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "symptomaticPeriod_mean, symptomaticPeriod_coeffvar = 4.0, 0.4\n",
+    "GAMMA   = 1 / gamma_dist(symptomaticPeriod_mean, symptomaticPeriod_coeffvar, N)\n",
+    "\n",
+    "infectiousPeriod = 1/LAMDA + 1/GAMMA\n",
+    "\n",
+    "dist_info([1/LAMDA, 1/GAMMA, 1/LAMDA+1/GAMMA], [\"pre-symptomatic period\", \"(a)symptomatic period\", \"total infectious period\"], plot=True, colors=['darkorange', 'crimson', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected onset-to-hospitalization periods (time in symptomatic state before entering hospitalized state for those with severe cases) and hospitalization-to-discharge periods (time in hospitalized state for those with non-fatal cases). The `eta` and `gamma_H` rates are calculated as the inverse of the expected onset-to-hospitalization periods and hospitalization-to-discharge periods assigned to each individual, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
+      "\n",
+      "hospitalization-to-discharge period:  mean = 11.38, std = 5.29, 95% CI = (3.49, 23.54)\n",
+      "\n",
+      "onset-to-discharge period:  mean = 22.51, std = 7.16, 95% CI = (10.62, 39.43)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar = 11.0, 0.45\n",
+    "ETA     = 1 / gamma_dist(onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar, N)\n",
+    "\n",
+    "hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar = 11.0, 0.45\n",
+    "GAMMA_H = 1 / gamma_dist(hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/ETA, 1/GAMMA_H, 1/ETA+1/GAMMA_H], [\"onset-to-hospitalization period\", \"hospitalization-to-discharge period\", \"onset-to-discharge period\"], plot=True, colors=['crimson', 'violet', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of hospitalization-to-death periods (time in hospitalized state for those with fatal cases). The `mu_H` rates are calculated as the inverse of the expected hospitalization-to-death periods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
+      "\n",
+      "hospitalization-to-death period:  mean = 6.87, std = 3.10, 95% CI = (2.01, 13.61)\n",
+      "\n",
+      "onset-to-death period:  mean = 17.99, std = 6.08, 95% CI = (8.34, 31.59)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar = 7.0, 0.45\n",
+    "MU_H    = 1 / gamma_dist(hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/ETA, 1/MU_H, 1/ETA+1/MU_H], [\"onset-to-hospitalization period\", \"hospitalization-to-death period\", \"onset-to-death period\"], plot=True, colors=['crimson', 'darkgray', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set severity parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the percentage of cases that are asymptomatic. This percentage of case will progress from the pre-symptomatic state to the asymptomatic state, rather than to the symptomatic state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_ASYMPTOMATIC = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the case hospitalization rate. The value used here is approximately the age-frequency-weighted average of age-stratified hospitalization rates for working age adults using data from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_HOSPITALIZED = 0.035"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the case fatality rate for hospitalized cases. The value used here is approximately the age-frequency-weighted average of age stratified hospitalization fatality rates for working age adults, again using figures from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_FATALITY = 0.08"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set transmission parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#stochastic-network-model-implementation) model considers two modes of disease transmission: a well-mixed mode of [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) and a contact network based mode of [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The propensity for a given individual to become exposed due to global transmission depends on the mean transmissibility of all infectious individuals in the population; the propensity for a given individual to become exposed due to local transmission depends on the pairwise transmissibilities between the focal node and its infectious contacts in the network (see [Transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#transmission) and [Model Equations](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#model-equations) for more information about these calculations). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The transmissibility parameter *β* can be related to the basic reproduction number *R<sub>0</sub>* (i.e., the expected number of new infections generated by a single infectious individual in a completely susceptible population) by the standard formula: *β = R<sub>0</sub>𝛾*. *R<sub>0</sub>* is a more interpretable parameter, so we specify transmissibility in terms of *R<sub>0</sub>* and then calculate the corresponding *β* values.\n",
+    "\n",
+    "First, we generate a distribution of individual *R<sub>0</sub>* values (i.e., the expected number of new infections generated by a single *particular* infectious individual in a completely susceptible population). Of course, this means that transmissibility is heterogeneous in this population. The coefficient of variation is an important parameter for the individual *R<sub>0</sub>* distribution in that it tunes the degree of superspreading in the heterogeneous transmissibility. The distribution used in this example has a relatively low coefficient of variation, so most individuals have around the same degree of transmissibility. But a higher coefficient of variation (e.g., 2.0) would give a long right tail in idividual transmissibility representing a small number of individuals contributing many cases while the majority cases contribute less than 1 on average when they are infectious."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Individual R0:  mean = 1.98, std = 0.41, 95% CI = (1.32, 2.89)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "R0_mean     = 2.0\n",
+    "R0_coeffvar = 0.2\n",
+    "\n",
+    "R0 = gamma_dist(R0_mean, R0_coeffvar, N)\n",
+    "\n",
+    "dist_info(R0, \"Individual R0\", bin_size=0.1, plot=True, colors='crimson')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Individuals are ultimately assigned an [*Individual Transmissibility Value*](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#transmissibility-parameters) (*β<sup>i</sup>*), which are stored in the `beta` attribute of the model object. \n",
+    "\n",
+    "The means of the Individual Transmissibility Values for infectious subpopulations are used to calculate the [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) terms. Individual Transmissibility Values may also be used to generate the Pairwise Transmissibility Values used for [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission) terms, as we will specify in a few steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BETA = 1/infectiousPeriod * R0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the stochastic network model, an individual comes into contact with a random individual from the population at large (e.g., in a public space) with probability *p* or with an individual from their set of close contacts with probability *(1-p)*. Transmission that occurs between an individual and the population at large is referred to as [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission), and transmission between an individual and one of their close contacts (network neighbors) is referred to as [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The parameter *p* defines the locality of the network: for *p=0* an individual only interacts with their close contacts, while *p=1* represents a uniformly mixed population.\n",
+    "\n",
+    "Here we set *p* to reflect 40% of interactions being with incidental or casual contacts outside their set of close contacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P_GLOBALINTXN = 0.4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Testing, Tracing, & Isolation (TTI) intervention protocol parameters:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the parameters that govern the Testing, Tracing, and Isolation protocol that is implemented by the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). The implementation of this TTI protocol and the interpretation of these parameters is desribed in detail on the [TTI Simulation Loop wiki page](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) (but these parameters are briefly explained as code comments below).\n",
+    "\n",
+    "**The scenario set up in the steps that follow involves the entire workforce being tested on a weekly basis, a 2-day test turn around time, 50% of symptomatic individuals self-reporting and getting tested within 1 day of onset, 30% of symptomatics self-isolating even without a positive test, and teams of detected positive cases being proactively isolated. A new exogenous exposures comes into the workplace about once a week.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INTERVENTION_START_PCT_INFECTED = 0/100\n",
+    "AVERAGE_INTRODUCTIONS_PER_DAY   = 1/14          # expected number of new exogenous exposures per day\n",
+    "\n",
+    "TESTING_CADENCE                 = 'weekly'      # how often to do testing (other than self-reporting symptomatics who can get tested any day)\n",
+    "PCT_TESTED_PER_DAY              = 1.0           # max daily test allotment defined as a percent of population size\n",
+    "TEST_FALSENEG_RATE              = 'temporal'    # test false negative rate, will use FN rate that varies with disease time\n",
+    "MAX_PCT_TESTS_FOR_SYMPTOMATICS  = 1.0           # max percent of daily test allotment to use on self-reporting symptomatics\n",
+    "MAX_PCT_TESTS_FOR_TRACES        = 0.0           # max percent of daily test allotment to use on contact traces\n",
+    "RANDOM_TESTING_DEGREE_BIAS      = 0             # magnitude of degree bias in random selections for testing, none here\n",
+    "\n",
+    "PCT_CONTACTS_TO_TRACE           = 0.0           # percentage of primary cases' contacts that are traced\n",
+    "TRACING_LAG                     = 2             # number of cadence testing days between primary tests and tracing tests\n",
+    "\n",
+    "ISOLATION_LAG_SYMPTOMATIC       = 1             # number of days between onset of symptoms and self-isolation of symptomatics\n",
+    "ISOLATION_LAG_POSITIVE          = 2             # test turn-around time (TAT): number of days between administration of test and isolation of positive cases\n",
+    "ISOLATION_LAG_CONTACT           = 0             # number of days between a contact being traced and that contact self-isolating\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set intervention compliance parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we specify the compliance rates (i.e., the percentage of individuals who are compliant) for each intervention type. See the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) documentation for more information about compliance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TESTING_COMPLIANCE_RATE_SYMPTOMATIC                  = 0.5    \n",
+    "TESTING_COMPLIANCE_RATE_TRACED                       = 0.0\n",
+    "TESTING_COMPLIANCE_RATE_RANDOM                       = 1.0  # Assume employee testing is mandatory, so 100% compliance\n",
+    "\n",
+    "TRACING_COMPLIANCE_RATE                              = 0.0\n",
+    "\n",
+    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL     = 0.3\n",
+    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE      = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL        = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE         = 0.8  # Isolate teams with a positive member, but suppose 20% of employees are essential workforce\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT           = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE  = 0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we randomly assign a `True/False` compliance to each individual according to the rates set above. Individuals whose compliance is set to `True` for a given intervention will participate in that intervention, individuals set to `False` will not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TESTING_COMPLIANCE_RANDOM                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_RANDOM)\n",
+    "TESTING_COMPLIANCE_TRACED                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_TRACED)\n",
+    "TESTING_COMPLIANCE_SYMPTOMATIC                   = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_SYMPTOMATIC)\n",
+    "\n",
+    "TRACING_COMPLIANCE                               = (numpy.random.rand(N) < TRACING_COMPLIANCE_RATE)\n",
+    "\n",
+    "ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL      = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL)\n",
+    "ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE       = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL         = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE          = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_CONTACT            = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE   = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\boaz\\PycharmProjects\\seirsplus\\seirsplus\\models.py:2143: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.delta_Q             = numpy.log(self.degree_Q)/numpy.log(numpy.mean(self.degree_Q)) if self.parameters['delta_Q'] is None else numpy.array(self.parameters['delta_Q']) if isinstance(self.parameters['delta_Q'], (list, numpy.ndarray)) else numpy.full(fill_value=self.parameters['delta_Q'], shape=(self.numNodes,1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ExtSEIRSNetworkModel(G=G_baseline, p=P_GLOBALINTXN,\n",
+    "                              beta=BETA, sigma=SIGMA, lamda=LAMDA, gamma=GAMMA, \n",
+    "                              gamma_asym=GAMMA, eta=ETA, gamma_H=GAMMA_H, mu_H=MU_H, \n",
+    "                              a=PCT_ASYMPTOMATIC, h=PCT_HOSPITALIZED, f=PCT_FATALITY,              \n",
+    "                              G_Q=G_quarantine, isolation_time=14,\n",
+    "                              initE=INIT_EXPOSED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the max simulation time to 300 days."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Execute the TTI simulation scenario by calling the `run_tti_sim()` function, which runs a custom simulation loop that implements the [TTI Simulation Protocol](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INTERVENTIONS @ t = 0.64 (4 (0.50%) infected)]\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'sort' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "\u001B[1;32m<ipython-input-28-c1364e940a88>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m run_tti_sim(model, T, \n\u001B[0m\u001B[0;32m      2\u001B[0m             \u001B[0mintervention_start_pct_infected\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mINTERVENTION_START_PCT_INFECTED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage_introductions_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mAVERAGE_INTRODUCTIONS_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m             \u001B[0mtesting_cadence\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_CADENCE\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpct_tested_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mPCT_TESTED_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtest_falseneg_rate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTEST_FALSENEG_RATE\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m             \u001B[0mtesting_compliance_symptomatic\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_SYMPTOMATIC\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_symptomatics\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_SYMPTOMATICS\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m             \u001B[0mtesting_compliance_traced\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_TRACED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_traces\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_TRACES\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\PycharmProjects\\seirsplus\\seirsplus\\sim_loops.py\u001B[0m in \u001B[0;36mrun_tti_sim\u001B[1;34m(model, T, intervention_start_pct_infected, average_introductions_per_day, testing_cadence, pct_tested_per_day, test_falseneg_rate, testing_compliance_symptomatic, max_pct_tests_for_symptomatics, testing_compliance_traced, max_pct_tests_for_traces, testing_compliance_random, random_testing_degree_bias, tracing_compliance, num_contacts_to_trace, pct_contacts_to_trace, tracing_lag, isolation_compliance_symptomatic_individual, isolation_compliance_symptomatic_groupmate, isolation_compliance_positive_individual, isolation_compliance_positive_groupmate, isolation_compliance_positive_contact, isolation_compliance_positive_contactgroupmate, isolation_lag_symptomatic, isolation_lag_positive, isolation_lag_contact, isolation_groups, cadence_testing_days, cadence_cycle_length, temporal_falseneg_rates, test_priority)\u001B[0m\n\u001B[0;32m    268\u001B[0m                             \u001B[1;32mif\u001B[0m \u001B[1;34m'last_tested'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtest_priority\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    269\u001B[0m                                 \u001B[1;31m# sort the pool according to the time they were last tested, breaking ties randomly\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 270\u001B[1;33m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mkey\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtestedTime\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcmp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0my\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m*\u001B[0m \u001B[1;36m2\u001B[0m \u001B[1;33m-\u001B[0m \u001B[1;36m1\u001B[0m  \u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0mnumRandomTests\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    271\u001B[0m                             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    272\u001B[0m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtestingPool\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnumpy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mchoice\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnumRandomTests\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mp\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mtestingPool_degreeWeights\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreplace\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'sort' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "run_tti_sim(model, T, \n",
+    "            intervention_start_pct_infected=INTERVENTION_START_PCT_INFECTED, average_introductions_per_day=AVERAGE_INTRODUCTIONS_PER_DAY,\n",
+    "            testing_cadence=TESTING_CADENCE, pct_tested_per_day=PCT_TESTED_PER_DAY, test_falseneg_rate=TEST_FALSENEG_RATE, \n",
+    "            testing_compliance_symptomatic=TESTING_COMPLIANCE_SYMPTOMATIC, max_pct_tests_for_symptomatics=MAX_PCT_TESTS_FOR_SYMPTOMATICS,\n",
+    "            testing_compliance_traced=TESTING_COMPLIANCE_TRACED, max_pct_tests_for_traces=MAX_PCT_TESTS_FOR_TRACES,\n",
+    "            testing_compliance_random=TESTING_COMPLIANCE_RANDOM, random_testing_degree_bias=RANDOM_TESTING_DEGREE_BIAS,\n",
+    "            tracing_compliance=TRACING_COMPLIANCE, pct_contacts_to_trace=PCT_CONTACTS_TO_TRACE, tracing_lag=TRACING_LAG,\n",
+    "            isolation_compliance_symptomatic_individual=ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL, isolation_compliance_symptomatic_groupmate=ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE, \n",
+    "            isolation_compliance_positive_individual=ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL, isolation_compliance_positive_groupmate=ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE,\n",
+    "            isolation_compliance_positive_contact=ISOLATION_COMPLIANCE_POSITIVE_CONTACT, isolation_compliance_positive_contactgroupmate=ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE,\n",
+    "            isolation_lag_symptomatic=ISOLATION_LAG_SYMPTOMATIC, isolation_lag_positive=ISOLATION_LAG_POSITIVE, \n",
+    "            isolation_groups=list(teams.values()),\n",
+    "            test_priority = 'last_tested')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total percent infected: 0.88%\n",
+      "total percent fatality: 0.00%\n",
+      "peak  pct hospitalized: 0.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_summary(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 864x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = model.figure_infections(combine_Q_infected=False, plot_Q_R='stacked', plot_Q_S='stacked')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file

From fd46ae2edb912c15fc2b36200c722d422db16a17 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 11:21:23 -0400
Subject: [PATCH 03/19] Fixed bug with sorting

---
 seirsplus/sim_loops.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 339aa5d..ae9006d 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -264,10 +264,11 @@ def run_tti_sim(model, T,
                         else:
                             testingPool_degreeWeights = numpy.power(testingPool_degrees,random_testing_degree_bias)/numpy.sum(numpy.power(testingPool_degrees,random_testing_degree_bias))
 
-                        if(len(testingPool) > 0):
+                        poolSize = len(testingPool)
+                        if(poolSize > 0):
                             if 'last_tested' in test_priority:
                                 # sort the pool according to the time they were last tested, breaking ties randomly
-                                randomSelection = sort(testingPool,key = lambda i: model.testedTime[i], cmp = lambda x,y: x-y if x-y else random.randint(0, 1) * 2 - 1  )[:numRandomTests]
+                                randomSelection = sorted(testingPool,key = lambda i: (model.testedTime[i], random.randint(0,poolSize*poolSize)))[:numRandomTests]
                             else:
                                 randomSelection = testingPool[numpy.random.choice(len(testingPool), numRandomTests, p=testingPool_degreeWeights, replace=False)]
 

From 51d3ad00c0c53b03d9c727a3d6ffd70014962538 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 11:26:52 -0400
Subject: [PATCH 04/19] Remove testing notebook from git tracking

---
 .gitignore                                    |   1 +
 .../Extended_SEIRS_Workplace_TTI_Demo.ipynb   |  20 +-
 ...EIRS_Workplace_TTI_Demo_timePriority.ipynb | 853 ------------------
 3 files changed, 3 insertions(+), 871 deletions(-)
 delete mode 100644 examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb

diff --git a/.gitignore b/.gitignore
index 76386e1..2cff9c4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 # ignore temporary swap files
 *.swp
 
+examples/Testing*.ipynb # ignore notebooks used for testing features
 .DS_Store
diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
index be4bb46..f7a63ec 100644
--- a/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
+++ b/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
@@ -31,10 +31,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "from seirsplus.models import *\n",
     "from seirsplus.networks import *\n",
@@ -44,20 +42,6 @@
     "import matplotlib.pyplot as pyplot"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *Alternatively, manually copy the code to your machine*\n",
-    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
-    "```python\n",
-    "from models import *\n",
-    "from networks import *\n",
-    "from sim_loops import *\n",
-    "from utilities import *\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3279,4 +3263,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
deleted file mode 100644
index 45a2eec..0000000
--- a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
+++ /dev/null
@@ -1,853 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extended SEIRS Workplace TTI Demo\n",
-    "\n",
-    "**In this demonstration we will explore the effect of testing, tracing, and isolation interventions on disease transmission in a workplace setting with a realistic contact network.**\n",
-    "\n",
-    "This notebook provides a demonstration of the functionality of the [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description) and the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). This notebook also offers a sandbox for starting to explore TTI scenarios of your own. \n",
-    "For a more thorough walkthrough of the model, simulation loop, and use of this package, refer to the [SEIRS+ Wiki](https://github.com/ryansmcgee/seirsplus/wiki)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installing and importing the model code\n",
-    "\n",
-    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
-    "\n",
-    "In this demo we will also use features from the `networks`, `sim_loops`, and `utilities` modules.\n",
-    "\n",
-    "#### Install the package using ```pip```\n",
-    "The package can be installed on your machine by entering this in the command line:\n",
-    "\n",
-    "```pip install seirsplus```\n",
-    "\n",
-    "Then, the ```models```, `networks`, `sim_loops`, and `utilities` modules can be imported as shown here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from seirsplus.models import *\n",
-    "from seirsplus.networks import *\n",
-    "from seirsplus.sim_loops import *\n",
-    "from seirsplus.utilities import *\n",
-    "import networkx\n",
-    "import matplotlib.pyplot as pyplot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *Alternatively, manually copy the code to your machine*\n",
-    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
-    "```python\n",
-    "from models import *\n",
-    "from networks import *\n",
-    "from sim_loops import *\n",
-    "from utilities import *\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set basic parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the workplace size and structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NUM_COHORTS              = 4\n",
-    "NUM_NODES_PER_COHORT     = 200\n",
-    "NUM_TEAMS_PER_COHORT     = 10\n",
-    "\n",
-    "MEAN_INTRACOHORT_DEGREE  = 6\n",
-    "PCT_CONTACTS_INTERCOHORT = 0.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N = NUM_NODES_PER_COHORT*NUM_COHORTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we set the initial prevalence to be a single case"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "INIT_EXPOSED = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------\n",
-    "\n",
-    "## Specifying contact networks\n",
-    "\n",
-    "This package implements models epidemic dynamics for populations with a structured [contact network](Extended-SEIRS-Model-Description#contact-networks). Individuals are represented as nodes in a network, and parameters, contacts, and interventions can be specified on a targeted individual basis. A graph specifying the contact network must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
-    "\n",
-    "This model also supports scenarios where individuals enter quarantine states in which their parameters and interactions may be different from baseline, and a separate graph defining the interactions for individuals in quarantine can be specified (i.e., the [quarantine contact network](Extended-SEIRS-Model-Description#quarantine-contacts)).\n",
-    "\n",
-    "### Workplace Contact Network\n",
-    "\n",
-    "Here we use the [**demographic community network generator**](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#workplace-network) defined in the SEIRS+ package. This function generates a contact network that resembles workplaces and other multi-level modular populations.\n",
-    "\n",
-    "[FARZ](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#farz-networks) network layers are generated to represent cohorts of employees (e.g., departments, floors, shifts). FARZ networks have a tunable community structure, so each cohort includes some number of communities, which can be thought to represent teams (i.e., groups of employees that work closely with each other). Employees may belong to more than one team (specified by a FARZ parameter), but employees belong to only one cohort. An employee's intra-team and intra-cohort contacts are defined by the FARZ cohort network they belong to. A specified percentage of each employee's total number of workplace contacts can be with individuals from other cohorts. An employee's inter-cohort contacts are drawn randomly from the pool of individuals outside their own cohort. \n",
-    "\n",
-    "The number of cohorts, number of employees per cohort, number of teams per cohort, number of teams employees belong to, mean intra-cohort degree, percent of within- and between-team connections, and percent of intra- and inter-cohort connections can be controlled with the arguments to the `generate_demographic_contact_network()` function (some of which are passed as [parameters to the FARZ generator](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#FARZ-parameters)).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Baseline:\n",
-      "Degree: mean = 11.11, std = 8.41, 95% CI = (1.00, 29.00)\n",
-      "        coeff var = 0.76\n",
-      "Assortativity:    0.28\n",
-      "Clustering coeff: 0.24\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G_baseline, cohorts, teams = generate_workplace_contact_network(\n",
-    "                                 num_cohorts=NUM_COHORTS, num_nodes_per_cohort=NUM_NODES_PER_COHORT, \n",
-    "                                 num_teams_per_cohort=NUM_TEAMS_PER_COHORT,\n",
-    "                                 mean_intracohort_degree=MEAN_INTRACOHORT_DEGREE, \n",
-    "                                 pct_contacts_intercohort=PCT_CONTACTS_INTERCOHORT,\n",
-    "                                 farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10, \n",
-    "                                              'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False})\n",
-    "\n",
-    "network_info(G_baseline, \"Baseline\", plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we define the quarantine contact network to be an empty network (i.e., no connections). This represents an assumption that an employee that is in a quarantine state makes no contact with anyone from their workplace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_quarantine = networkx.classes.function.create_empty_copy(G_baseline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Specifying parameters\n",
-    "\n",
-    "**_The parameter values used in this notebook reflect rough estimates of parameter values for the COVID-19 epidemic (as of 9 Aug 2020)._**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set disease progression rate parameters:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Parameter values are assigned to members of the population on an individual basis. Parameter values can be [specified to the `ExtSEIRSNetworkModel`](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#specifying-parameters) by providing a list of values that gives the *N* values to assign to each individual. The population may be either homogeneous or heterogeneous for a given parameter at the user's discretion. \n",
-    "\n",
-    "**Here we generate distributions of values for each parameter, thus specifying a realistically heterogeneous population.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected latent periods (time in Exposed state) and presymptomatic periods (time in Pre-symptomatic infectious state). The `sigma` and `lamda` rates are calculated as the inverse of the expected exposed and pre-symptomatic periods assigned to each individual, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "latent period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
-      "\n",
-      "pre-symptomatic period:  mean = 2.94, std = 1.72, 95% CI = (0.64, 7.12)\n",
-      "\n",
-      "total incubation period:  mean = 5.14, std = 2.03, 95% CI = (2.17, 10.02)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "latentPeriod_mean, latentPeriod_coeffvar = 3.0, 0.6\n",
-    "SIGMA   = 1 / gamma_dist(latentPeriod_mean, latentPeriod_coeffvar, N)\n",
-    "\n",
-    "presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar = 2.2, 0.5\n",
-    "LAMDA   = 1 / gamma_dist(presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/LAMDA, 1/SIGMA, 1/LAMDA+1/SIGMA], [\"latent period\", \"pre-symptomatic period\", \"total incubation period\"], plot=True, colors=['gold', 'darkorange', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected (a)symptomatic periods (time in symptomatic or asymptomatic state). The `gamma` rates are calculated as the inverse of the expected (a)symptomatic periods assigned to each individual. \n",
-    "\n",
-    "The expected total infectious period for each individual is the sum of their expected pre-symptomatic and (a)symptomatic periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pre-symptomatic period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
-      "\n",
-      "(a)symptomatic period:  mean = 4.01, std = 1.54, 95% CI = (1.64, 7.36)\n",
-      "\n",
-      "total infectious period:  mean = 6.20, std = 1.87, 95% CI = (3.09, 10.45)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "symptomaticPeriod_mean, symptomaticPeriod_coeffvar = 4.0, 0.4\n",
-    "GAMMA   = 1 / gamma_dist(symptomaticPeriod_mean, symptomaticPeriod_coeffvar, N)\n",
-    "\n",
-    "infectiousPeriod = 1/LAMDA + 1/GAMMA\n",
-    "\n",
-    "dist_info([1/LAMDA, 1/GAMMA, 1/LAMDA+1/GAMMA], [\"pre-symptomatic period\", \"(a)symptomatic period\", \"total infectious period\"], plot=True, colors=['darkorange', 'crimson', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected onset-to-hospitalization periods (time in symptomatic state before entering hospitalized state for those with severe cases) and hospitalization-to-discharge periods (time in hospitalized state for those with non-fatal cases). The `eta` and `gamma_H` rates are calculated as the inverse of the expected onset-to-hospitalization periods and hospitalization-to-discharge periods assigned to each individual, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
-      "\n",
-      "hospitalization-to-discharge period:  mean = 11.38, std = 5.29, 95% CI = (3.49, 23.54)\n",
-      "\n",
-      "onset-to-discharge period:  mean = 22.51, std = 7.16, 95% CI = (10.62, 39.43)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar = 11.0, 0.45\n",
-    "ETA     = 1 / gamma_dist(onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar, N)\n",
-    "\n",
-    "hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar = 11.0, 0.45\n",
-    "GAMMA_H = 1 / gamma_dist(hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/ETA, 1/GAMMA_H, 1/ETA+1/GAMMA_H], [\"onset-to-hospitalization period\", \"hospitalization-to-discharge period\", \"onset-to-discharge period\"], plot=True, colors=['crimson', 'violet', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of hospitalization-to-death periods (time in hospitalized state for those with fatal cases). The `mu_H` rates are calculated as the inverse of the expected hospitalization-to-death periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
-      "\n",
-      "hospitalization-to-death period:  mean = 6.87, std = 3.10, 95% CI = (2.01, 13.61)\n",
-      "\n",
-      "onset-to-death period:  mean = 17.99, std = 6.08, 95% CI = (8.34, 31.59)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar = 7.0, 0.45\n",
-    "MU_H    = 1 / gamma_dist(hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/ETA, 1/MU_H, 1/ETA+1/MU_H], [\"onset-to-hospitalization period\", \"hospitalization-to-death period\", \"onset-to-death period\"], plot=True, colors=['crimson', 'darkgray', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set severity parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the percentage of cases that are asymptomatic. This percentage of case will progress from the pre-symptomatic state to the asymptomatic state, rather than to the symptomatic state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_ASYMPTOMATIC = 0.25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the case hospitalization rate. The value used here is approximately the age-frequency-weighted average of age-stratified hospitalization rates for working age adults using data from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_HOSPITALIZED = 0.035"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the case fatality rate for hospitalized cases. The value used here is approximately the age-frequency-weighted average of age stratified hospitalization fatality rates for working age adults, again using figures from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_FATALITY = 0.08"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set transmission parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#stochastic-network-model-implementation) model considers two modes of disease transmission: a well-mixed mode of [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) and a contact network based mode of [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The propensity for a given individual to become exposed due to global transmission depends on the mean transmissibility of all infectious individuals in the population; the propensity for a given individual to become exposed due to local transmission depends on the pairwise transmissibilities between the focal node and its infectious contacts in the network (see [Transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#transmission) and [Model Equations](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#model-equations) for more information about these calculations). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The transmissibility parameter *β* can be related to the basic reproduction number *R<sub>0</sub>* (i.e., the expected number of new infections generated by a single infectious individual in a completely susceptible population) by the standard formula: *β = R<sub>0</sub>𝛾*. *R<sub>0</sub>* is a more interpretable parameter, so we specify transmissibility in terms of *R<sub>0</sub>* and then calculate the corresponding *β* values.\n",
-    "\n",
-    "First, we generate a distribution of individual *R<sub>0</sub>* values (i.e., the expected number of new infections generated by a single *particular* infectious individual in a completely susceptible population). Of course, this means that transmissibility is heterogeneous in this population. The coefficient of variation is an important parameter for the individual *R<sub>0</sub>* distribution in that it tunes the degree of superspreading in the heterogeneous transmissibility. The distribution used in this example has a relatively low coefficient of variation, so most individuals have around the same degree of transmissibility. But a higher coefficient of variation (e.g., 2.0) would give a long right tail in idividual transmissibility representing a small number of individuals contributing many cases while the majority cases contribute less than 1 on average when they are infectious."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Individual R0:  mean = 1.98, std = 0.41, 95% CI = (1.32, 2.89)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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OiGq6iI6JiKaIaIyIxrq6up7swszMKkjT7fNZ4NyIeKPKfV8GzJT0R8BgYJikfwZ2SRoTETskjQF2d7kXMzM76dKc+b9IaXiHqkTEX0dEfUQ0ADcAP4iIm4CVwIJktQXAimr3bWZmJybNmf8R4DlJ6/jtgd1S3epZxhJguaRbgdeAOT3cj5mZ9VCa8P9O8uqxiFhP6a4eImIPcPWJ7M/MzE5MmoHdluVRiJmZ5SfNN3y3UhrC+bdExDmZVGRmZplL0+3T2OHzYEp99Gnv8zczs16o27t9ImJPh9frEbEUuCqH2szMLCNpun0u6TA5gNJfAqdnVpGZmWUuTbfP/+rw+TDJSJyZVGNmZrlIc7fPlXkUYmZm+UnT7fM+4DqOH8//i9mVZWZmWUrT7bMC2EdpQLd3ulnXzMz6gDThXx8R0zOvxMzMcpNmYLd/lfThzCsxM7PcpDnzvxy4Ofmm7zuAgIiIizKtzArryN59tB84WHF5tLXlWE3f0rZ9Z8VlA4YO8QPe7Zg04f+HmVdh1kH7gYO8tfTBisuH3eY7jctpP/QO++9dXnH58EXzHf52TJpbPV/NoxAzM8tPmj5/MzPrZxz+ZmYFlFn4Sxos6RlJz0t6UdLfJvPPlLRa0pbkfURWNZiZWXlZnvm/A1wVEZOAycB0SdOAxcDaiBgPrE2mzcwsR5mFf5QcSCYHJa8AZgFHnw62DJidVQ1mZlZepn3+kgZKeg7YDayOiKeB0RGxAyB5H1Vh24WSmiU1t7a2ZlmmmVnhZBr+EXEkIiYD9cBUSRdWsW1TRDRGRGNdXV12RZqZFVAud/tExFvAemA6sEvSGIDkfXceNZiZ2XuyvNunTtLw5PMQ4OPAZmAlsCBZbQGlUUPNzCxHaYZ36KkxwDJJAyn9klkeEU9IegpYLulW4DVKD4Q3M7McZRb+EfFz4OIy8/cAV2d1XDMz656/4WtmVkBZdvuYVdTVsM0estksew5/q4muhm32kM1m2XP4mxVIVw970aBTiLbDFZf7YTD9i8PfrCC6e9jLsNvm+mEwBeILvmZmBeTwNzMrIIe/mVkBOfzNzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAHP5mZgXk8DczK6Asn+Q1VtI6SZskvSjpjmT+mZJWS9qSvI/IqgYzMysvyzP/w8B/i4gPAdOAz0i6AFgMrI2I8cDaZNrMzHKUWfhHxI6I2Jh8/g2wCfggMAtYlqy2DJidVQ1mZlZeLn3+khooPdLxaWB0ROyA0i8IYFSFbRZKapbU3NramkeZZmaFkXn4SxoKfAtYFBH7024XEU0R0RgRjXV1ddkVaGZWQJmGv6RBlIL/GxHxWDJ7l6QxyfIxwO4sazAzs+NlebePgK8DmyLi7zssWgksSD4vAFZkVYOZmZWX5ZO8LgPmA7+Q9Fwy72+AJcBySbcCrwFzMqzBzMzKyCz8I+L/Aaqw+OqsjmtmZt3zN3zNzArI4W9mVkAOfzOzAnL4m5kVkMPfzKyAHP5mZgXk8DczKyCHv5lZATn8zcwKyOFvZlZADn8zswJy+JuZFZDD38ysgBz+ZmYFlOV4/mZWIEf27qP9wMGyywYMHcLAEWfkXJF1JbPwl3QfMAPYHREXJvPOBB4BGoBtwNyI2JtVDWaWn/YDB3lr6YNllw1fNN/h38tk2e1zPzC907zFwNqIGA+sTabNzCxnmYV/RPwIeLPT7FnAsuTzMmB2Vsc3M7PK8u7zHx0ROwAiYoekUZVWlLQQWAgwbty4nMozs660bd9ZcVm0teVYiZ2oXnvBNyKagCaAxsbGqHE5ZoXXfugd9t+7vOLyYbfNzbEaO1F53+q5S9IYgOR9d87HNzMz8j/zXwksAJYk7ytyPr7lpKvb/sBdBGa1luWtng8BVwAjJbUAd1EK/eWSbgVeA+ZkdXyrra5u+wN3EZjVWmbhHxE3Vlh0dVbHNLPeq6uLxf4SWP567QVfM+s/urtY7C+B5c9j+5iZFZDD38ysgBz+ZmYF5D5/M+v1urt12BeMq+fwN7Ner7tbh33BuHru9jEzKyCHv5lZATn8zcwKyH3+1iMeu8esb3P4W4947B6zvs3hb2X5zN7y5gfF5Mvhb2X5zN7y5AfF5M8XfM3MCshn/v1YV103/kakFYm/IXw8h38/1lXXjb8RaUXibwgfrybhL2k68A/AQOBrEbGkFnUUnS+wmRVX7uEvaSBwD/D7QAvwM0krI+KlvGspMl9gMyu2WlzwnQr8KiJeiYh3gYeBWTWow8yssBQR+R5Quh6YHhF/lkzPB/5jRNzeab2FwMJk8nzglz085EjgjR5umzXX1jOurWdcW8/05dp+NyLqyi2oRZ+/ysw77jdQRDQBTSd8MKk5IhpPdD9ZcG0949p6xrX1TH+trRbdPi3A2A7T9cCva1CHmVlh1SL8fwaMl3S2pFOBG4CVNajDzKywcu/2iYjDkm4HvkfpVs/7IuLFDA95wl1HGXJtPePaesa19Uy/rC33C75mZlZ7HtvHzKyAHP5mZgXUb8Jf0nRJv5T0K0mLyyyXpLuT5T+XdEkvqu0KSfskPZe87syprvsk7Zb0QoXltWyz7mqrSZslxx4raZ2kTZJelHRHmXVq0nYpa6vVz9tgSc9Iej6p7W/LrFOrdktTW81+5pLjD5T0rKQnyiyrvt0ios+/KF04fhk4BzgVeB64oNM6fwQ8Sel7BtOAp3tRbVcAT9Sg3T4KXAK8UGF5TdosZW01abPk2GOAS5LPpwP/1ot+3tLUVqufNwFDk8+DgKeBab2k3dLUVrOfueT4nwW+Wa6GnrRbfznzTzNkxCzggSj5KTBc0pheUltNRMSPgDe7WKVWbZamtpqJiB0RsTH5/BtgE/DBTqvVpO1S1lYTSVscSCYHJa/Od5zUqt3S1FYzkuqBa4GvVVil6nbrL+H/QWB7h+kWjv+BT7NOFtIe99LkT84nJU3Moa40atVmadW8zSQ1ABdTOlPsqOZt10VtUKO2S7oungN2A6sjote0W4raoHY/c0uBvwLaKyyvut36S/inGTIi1bASGUhz3I2UxuCYBPxv4DuZV5VOrdosjZq3maShwLeARRGxv/PiMpvk1nbd1FaztouIIxExmdI3+6dKurDTKjVrtxS11aTdJM0AdkfEhq5WKzOvy3brL+GfZsiIWg0r0e1xI2L/0T85I+K7wCBJI3OorTu9diiOWreZpEGUwvUbEfFYmVVq1nbd1VbrtkuO+xawHpjeaVHNf+Yq1VbDdrsMmClpG6Vu46sk/XOndaput/4S/mmGjFgJ/GlyVXwasC8idvSG2iT9jiQln6dS+nfZk0Nt3alVm3Wrlm2WHPfrwKaI+PsKq9Wk7dLUVqu2k1QnaXjyeQjwcWBzp9Vq1W7d1lardouIv46I+ohooJQfP4iImzqtVnW79YvHOEaFISMk3ZYsvxf4LqUr4r8C3gZu6UW1XQ/8Z0mHgYPADZFcws+SpIco3cEwUlILcBelC101bbOUtdWkzRKXAfOBXyR9xAB/A4zrUF+t2i5NbbVquzHAMpUe6DQAWB4RT/SG/09T1lbLn7njnGi7eXgHM7MC6i/dPmZmVgWHv5lZATn8zcwKyOFvZlZADn8zswJy+JuZFZDD38ysgP4/727GnGgzKagAAAAASUVORK5CYII=\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "R0_mean     = 2.0\n",
-    "R0_coeffvar = 0.2\n",
-    "\n",
-    "R0 = gamma_dist(R0_mean, R0_coeffvar, N)\n",
-    "\n",
-    "dist_info(R0, \"Individual R0\", bin_size=0.1, plot=True, colors='crimson')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Individuals are ultimately assigned an [*Individual Transmissibility Value*](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#transmissibility-parameters) (*β<sup>i</sup>*), which are stored in the `beta` attribute of the model object. \n",
-    "\n",
-    "The means of the Individual Transmissibility Values for infectious subpopulations are used to calculate the [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) terms. Individual Transmissibility Values may also be used to generate the Pairwise Transmissibility Values used for [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission) terms, as we will specify in a few steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BETA = 1/infectiousPeriod * R0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the stochastic network model, an individual comes into contact with a random individual from the population at large (e.g., in a public space) with probability *p* or with an individual from their set of close contacts with probability *(1-p)*. Transmission that occurs between an individual and the population at large is referred to as [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission), and transmission between an individual and one of their close contacts (network neighbors) is referred to as [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The parameter *p* defines the locality of the network: for *p=0* an individual only interacts with their close contacts, while *p=1* represents a uniformly mixed population.\n",
-    "\n",
-    "Here we set *p* to reflect 40% of interactions being with incidental or casual contacts outside their set of close contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P_GLOBALINTXN = 0.4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Testing, Tracing, & Isolation (TTI) intervention protocol parameters:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the parameters that govern the Testing, Tracing, and Isolation protocol that is implemented by the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). The implementation of this TTI protocol and the interpretation of these parameters is desribed in detail on the [TTI Simulation Loop wiki page](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) (but these parameters are briefly explained as code comments below).\n",
-    "\n",
-    "**The scenario set up in the steps that follow involves the entire workforce being tested on a weekly basis, a 2-day test turn around time, 50% of symptomatic individuals self-reporting and getting tested within 1 day of onset, 30% of symptomatics self-isolating even without a positive test, and teams of detected positive cases being proactively isolated. A new exogenous exposures comes into the workplace about once a week.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "INTERVENTION_START_PCT_INFECTED = 0/100\n",
-    "AVERAGE_INTRODUCTIONS_PER_DAY   = 1/14          # expected number of new exogenous exposures per day\n",
-    "\n",
-    "TESTING_CADENCE                 = 'weekly'      # how often to do testing (other than self-reporting symptomatics who can get tested any day)\n",
-    "PCT_TESTED_PER_DAY              = 1.0           # max daily test allotment defined as a percent of population size\n",
-    "TEST_FALSENEG_RATE              = 'temporal'    # test false negative rate, will use FN rate that varies with disease time\n",
-    "MAX_PCT_TESTS_FOR_SYMPTOMATICS  = 1.0           # max percent of daily test allotment to use on self-reporting symptomatics\n",
-    "MAX_PCT_TESTS_FOR_TRACES        = 0.0           # max percent of daily test allotment to use on contact traces\n",
-    "RANDOM_TESTING_DEGREE_BIAS      = 0             # magnitude of degree bias in random selections for testing, none here\n",
-    "\n",
-    "PCT_CONTACTS_TO_TRACE           = 0.0           # percentage of primary cases' contacts that are traced\n",
-    "TRACING_LAG                     = 2             # number of cadence testing days between primary tests and tracing tests\n",
-    "\n",
-    "ISOLATION_LAG_SYMPTOMATIC       = 1             # number of days between onset of symptoms and self-isolation of symptomatics\n",
-    "ISOLATION_LAG_POSITIVE          = 2             # test turn-around time (TAT): number of days between administration of test and isolation of positive cases\n",
-    "ISOLATION_LAG_CONTACT           = 0             # number of days between a contact being traced and that contact self-isolating\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set intervention compliance parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we specify the compliance rates (i.e., the percentage of individuals who are compliant) for each intervention type. See the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) documentation for more information about compliance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TESTING_COMPLIANCE_RATE_SYMPTOMATIC                  = 0.5    \n",
-    "TESTING_COMPLIANCE_RATE_TRACED                       = 0.0\n",
-    "TESTING_COMPLIANCE_RATE_RANDOM                       = 1.0  # Assume employee testing is mandatory, so 100% compliance\n",
-    "\n",
-    "TRACING_COMPLIANCE_RATE                              = 0.0\n",
-    "\n",
-    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL     = 0.3\n",
-    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE      = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL        = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE         = 0.8  # Isolate teams with a positive member, but suppose 20% of employees are essential workforce\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT           = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE  = 0.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we randomly assign a `True/False` compliance to each individual according to the rates set above. Individuals whose compliance is set to `True` for a given intervention will participate in that intervention, individuals set to `False` will not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TESTING_COMPLIANCE_RANDOM                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_RANDOM)\n",
-    "TESTING_COMPLIANCE_TRACED                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_TRACED)\n",
-    "TESTING_COMPLIANCE_SYMPTOMATIC                   = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_SYMPTOMATIC)\n",
-    "\n",
-    "TRACING_COMPLIANCE                               = (numpy.random.rand(N) < TRACING_COMPLIANCE_RATE)\n",
-    "\n",
-    "ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL      = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL)\n",
-    "ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE       = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL         = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE          = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_CONTACT            = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE   = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\boaz\\PycharmProjects\\seirsplus\\seirsplus\\models.py:2143: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.delta_Q             = numpy.log(self.degree_Q)/numpy.log(numpy.mean(self.degree_Q)) if self.parameters['delta_Q'] is None else numpy.array(self.parameters['delta_Q']) if isinstance(self.parameters['delta_Q'], (list, numpy.ndarray)) else numpy.full(fill_value=self.parameters['delta_Q'], shape=(self.numNodes,1))\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = ExtSEIRSNetworkModel(G=G_baseline, p=P_GLOBALINTXN,\n",
-    "                              beta=BETA, sigma=SIGMA, lamda=LAMDA, gamma=GAMMA, \n",
-    "                              gamma_asym=GAMMA, eta=ETA, gamma_H=GAMMA_H, mu_H=MU_H, \n",
-    "                              a=PCT_ASYMPTOMATIC, h=PCT_HOSPITALIZED, f=PCT_FATALITY,              \n",
-    "                              G_Q=G_quarantine, isolation_time=14,\n",
-    "                              initE=INIT_EXPOSED)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running the model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the max simulation time to 300 days."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T = 300"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Execute the TTI simulation scenario by calling the `run_tti_sim()` function, which runs a custom simulation loop that implements the [TTI Simulation Protocol](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INTERVENTIONS @ t = 0.64 (4 (0.50%) infected)]\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'sort' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "\u001B[1;32m<ipython-input-28-c1364e940a88>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m run_tti_sim(model, T, \n\u001B[0m\u001B[0;32m      2\u001B[0m             \u001B[0mintervention_start_pct_infected\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mINTERVENTION_START_PCT_INFECTED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage_introductions_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mAVERAGE_INTRODUCTIONS_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m             \u001B[0mtesting_cadence\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_CADENCE\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpct_tested_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mPCT_TESTED_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtest_falseneg_rate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTEST_FALSENEG_RATE\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m             \u001B[0mtesting_compliance_symptomatic\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_SYMPTOMATIC\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_symptomatics\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_SYMPTOMATICS\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m             \u001B[0mtesting_compliance_traced\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_TRACED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_traces\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_TRACES\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\PycharmProjects\\seirsplus\\seirsplus\\sim_loops.py\u001B[0m in \u001B[0;36mrun_tti_sim\u001B[1;34m(model, T, intervention_start_pct_infected, average_introductions_per_day, testing_cadence, pct_tested_per_day, test_falseneg_rate, testing_compliance_symptomatic, max_pct_tests_for_symptomatics, testing_compliance_traced, max_pct_tests_for_traces, testing_compliance_random, random_testing_degree_bias, tracing_compliance, num_contacts_to_trace, pct_contacts_to_trace, tracing_lag, isolation_compliance_symptomatic_individual, isolation_compliance_symptomatic_groupmate, isolation_compliance_positive_individual, isolation_compliance_positive_groupmate, isolation_compliance_positive_contact, isolation_compliance_positive_contactgroupmate, isolation_lag_symptomatic, isolation_lag_positive, isolation_lag_contact, isolation_groups, cadence_testing_days, cadence_cycle_length, temporal_falseneg_rates, test_priority)\u001B[0m\n\u001B[0;32m    268\u001B[0m                             \u001B[1;32mif\u001B[0m \u001B[1;34m'last_tested'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtest_priority\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    269\u001B[0m                                 \u001B[1;31m# sort the pool according to the time they were last tested, breaking ties randomly\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 270\u001B[1;33m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mkey\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtestedTime\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcmp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0my\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m*\u001B[0m \u001B[1;36m2\u001B[0m \u001B[1;33m-\u001B[0m \u001B[1;36m1\u001B[0m  \u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0mnumRandomTests\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    271\u001B[0m                             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    272\u001B[0m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtestingPool\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnumpy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mchoice\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnumRandomTests\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mp\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mtestingPool_degreeWeights\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreplace\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;31mNameError\u001B[0m: name 'sort' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "run_tti_sim(model, T, \n",
-    "            intervention_start_pct_infected=INTERVENTION_START_PCT_INFECTED, average_introductions_per_day=AVERAGE_INTRODUCTIONS_PER_DAY,\n",
-    "            testing_cadence=TESTING_CADENCE, pct_tested_per_day=PCT_TESTED_PER_DAY, test_falseneg_rate=TEST_FALSENEG_RATE, \n",
-    "            testing_compliance_symptomatic=TESTING_COMPLIANCE_SYMPTOMATIC, max_pct_tests_for_symptomatics=MAX_PCT_TESTS_FOR_SYMPTOMATICS,\n",
-    "            testing_compliance_traced=TESTING_COMPLIANCE_TRACED, max_pct_tests_for_traces=MAX_PCT_TESTS_FOR_TRACES,\n",
-    "            testing_compliance_random=TESTING_COMPLIANCE_RANDOM, random_testing_degree_bias=RANDOM_TESTING_DEGREE_BIAS,\n",
-    "            tracing_compliance=TRACING_COMPLIANCE, pct_contacts_to_trace=PCT_CONTACTS_TO_TRACE, tracing_lag=TRACING_LAG,\n",
-    "            isolation_compliance_symptomatic_individual=ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL, isolation_compliance_symptomatic_groupmate=ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE, \n",
-    "            isolation_compliance_positive_individual=ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL, isolation_compliance_positive_groupmate=ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE,\n",
-    "            isolation_compliance_positive_contact=ISOLATION_COMPLIANCE_POSITIVE_CONTACT, isolation_compliance_positive_contactgroupmate=ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE,\n",
-    "            isolation_lag_symptomatic=ISOLATION_LAG_SYMPTOMATIC, isolation_lag_positive=ISOLATION_LAG_POSITIVE, \n",
-    "            isolation_groups=list(teams.values()),\n",
-    "            test_priority = 'last_tested')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total percent infected: 0.88%\n",
-      "total percent fatality: 0.00%\n",
-      "peak  pct hospitalized: 0.00%\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_summary(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = model.figure_infections(combine_Q_infected=False, plot_Q_R='stacked', plot_Q_S='stacked')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file

From 7da545a6eb4f356eaf5b3d7dfc0c9ffc30dbce5d Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 13:18:34 -0400
Subject: [PATCH 05/19] Revert "Remove testing notebook from git tracking"

This reverts commit 51d3ad00
---
 .gitignore                                    |   1 -
 .../Extended_SEIRS_Workplace_TTI_Demo.ipynb   |  18 +-
 ...EIRS_Workplace_TTI_Demo_timePriority.ipynb | 853 ++++++++++++++++++
 3 files changed, 870 insertions(+), 2 deletions(-)
 create mode 100644 examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb

diff --git a/.gitignore b/.gitignore
index 2cff9c4..76386e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,4 @@
 # ignore temporary swap files
 *.swp
 
-examples/Testing*.ipynb # ignore notebooks used for testing features
 .DS_Store
diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
index f7a63ec..6e04b19 100644
--- a/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
+++ b/examples/Extended_SEIRS_Workplace_TTI_Demo.ipynb
@@ -31,8 +31,10 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
+   "outputs": [],
    "source": [
     "from seirsplus.models import *\n",
     "from seirsplus.networks import *\n",
@@ -42,6 +44,20 @@
     "import matplotlib.pyplot as pyplot"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *Alternatively, manually copy the code to your machine*\n",
+    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
+    "```python\n",
+    "from models import *\n",
+    "from networks import *\n",
+    "from sim_loops import *\n",
+    "from utilities import *\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
new file mode 100644
index 0000000..45a2eec
--- /dev/null
+++ b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
@@ -0,0 +1,853 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extended SEIRS Workplace TTI Demo\n",
+    "\n",
+    "**In this demonstration we will explore the effect of testing, tracing, and isolation interventions on disease transmission in a workplace setting with a realistic contact network.**\n",
+    "\n",
+    "This notebook provides a demonstration of the functionality of the [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description) and the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). This notebook also offers a sandbox for starting to explore TTI scenarios of your own. \n",
+    "For a more thorough walkthrough of the model, simulation loop, and use of this package, refer to the [SEIRS+ Wiki](https://github.com/ryansmcgee/seirsplus/wiki)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Installing and importing the model code\n",
+    "\n",
+    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
+    "\n",
+    "In this demo we will also use features from the `networks`, `sim_loops`, and `utilities` modules.\n",
+    "\n",
+    "#### Install the package using ```pip```\n",
+    "The package can be installed on your machine by entering this in the command line:\n",
+    "\n",
+    "```pip install seirsplus```\n",
+    "\n",
+    "Then, the ```models```, `networks`, `sim_loops`, and `utilities` modules can be imported as shown here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from seirsplus.models import *\n",
+    "from seirsplus.networks import *\n",
+    "from seirsplus.sim_loops import *\n",
+    "from seirsplus.utilities import *\n",
+    "import networkx\n",
+    "import matplotlib.pyplot as pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### *Alternatively, manually copy the code to your machine*\n",
+    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
+    "```python\n",
+    "from models import *\n",
+    "from networks import *\n",
+    "from sim_loops import *\n",
+    "from utilities import *\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set basic parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the workplace size and structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NUM_COHORTS              = 4\n",
+    "NUM_NODES_PER_COHORT     = 200\n",
+    "NUM_TEAMS_PER_COHORT     = 10\n",
+    "\n",
+    "MEAN_INTRACOHORT_DEGREE  = 6\n",
+    "PCT_CONTACTS_INTERCOHORT = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = NUM_NODES_PER_COHORT*NUM_COHORTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we set the initial prevalence to be a single case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INIT_EXPOSED = 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------\n",
+    "\n",
+    "## Specifying contact networks\n",
+    "\n",
+    "This package implements models epidemic dynamics for populations with a structured [contact network](Extended-SEIRS-Model-Description#contact-networks). Individuals are represented as nodes in a network, and parameters, contacts, and interventions can be specified on a targeted individual basis. A graph specifying the contact network must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
+    "\n",
+    "This model also supports scenarios where individuals enter quarantine states in which their parameters and interactions may be different from baseline, and a separate graph defining the interactions for individuals in quarantine can be specified (i.e., the [quarantine contact network](Extended-SEIRS-Model-Description#quarantine-contacts)).\n",
+    "\n",
+    "### Workplace Contact Network\n",
+    "\n",
+    "Here we use the [**demographic community network generator**](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#workplace-network) defined in the SEIRS+ package. This function generates a contact network that resembles workplaces and other multi-level modular populations.\n",
+    "\n",
+    "[FARZ](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#farz-networks) network layers are generated to represent cohorts of employees (e.g., departments, floors, shifts). FARZ networks have a tunable community structure, so each cohort includes some number of communities, which can be thought to represent teams (i.e., groups of employees that work closely with each other). Employees may belong to more than one team (specified by a FARZ parameter), but employees belong to only one cohort. An employee's intra-team and intra-cohort contacts are defined by the FARZ cohort network they belong to. A specified percentage of each employee's total number of workplace contacts can be with individuals from other cohorts. An employee's inter-cohort contacts are drawn randomly from the pool of individuals outside their own cohort. \n",
+    "\n",
+    "The number of cohorts, number of employees per cohort, number of teams per cohort, number of teams employees belong to, mean intra-cohort degree, percent of within- and between-team connections, and percent of intra- and inter-cohort connections can be controlled with the arguments to the `generate_demographic_contact_network()` function (some of which are passed as [parameters to the FARZ generator](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#FARZ-parameters)).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline:\n",
+      "Degree: mean = 11.11, std = 8.41, 95% CI = (1.00, 29.00)\n",
+      "        coeff var = 0.76\n",
+      "Assortativity:    0.28\n",
+      "Clustering coeff: 0.24\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD4CAYAAADrRI2NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAY6ElEQVR4nO3dfXRU9Z3H8feXEORBOEUIbCylUaugSIgYWFBqQeShFUFXQWmtqLh4eqgIVgW03XZ79Bzcup7Wau1GZU231oWjpXAs6xNiWS3aEnURAa26SKNZCBRBwmPid/+YGxwgmdw83JnJ3M/rnJyZe2fuvd/8IJ/c/OZ3f9fcHRERiY8OmS5ARETSS8EvIhIzCn4RkZhR8IuIxIyCX0QkZjpmuoAwevfu7UVFRZkuQ0SkXamoqNjh7gXHrm8XwV9UVMS6desyXYaISLtiZh82tF5dPSIiMaPgFxGJGQW/iEjMtIs+fhFpW4cPH6ayspIDBw5kuhRpA507d6Zfv37k5+eHer+CXySGKisr6d69O0VFRZhZpsuRVnB3du7cSWVlJaecckqobdTVIxJDBw4coFevXgr9HGBm9OrVq1l/vSn4RWJKoZ87mvtvqeAXEYkZ9fGLCLv2HaLmYG2b7a/bCR3p2bVTyvfk5eUxePBg3J28vDweeOABzjvvvDar4dprr2XSpElcccUV3HDDDdxyyy2cddZZbbZ/gNGjR3PvvfdSWlrapvuNmoK/hZr6QQnzH18kW9QcrOXB1e+32f5mjzmtyf//Xbp04c033wTg2WefZeHChfzhD39osxqSPfLII5Hst63U1tbSsWP64lhdPS1U/4PS2Fdbnj2J5Lo9e/bQs2dPAPbu3cvYsWMZOnQogwcPZvny5QDU1NRw8cUXM2TIEM4++2yWLFkCQEVFBV/72tc499xzmTBhAlVVVcftf/To0UemfTnxxBO58847GTJkCCNGjGDbtm0AVFdXc/nllzNs2DCGDRvGK6+8ctx+9u/fz1VXXUVxcTFXXnkl+/fvP/Lac889x8iRIxk6dChTp05l7969AKxcuZKBAwcyatQo5syZw6RJkwD40Y9+xKxZsxg/fjzXXHNNo8evqanh+uuvZ9iwYZxzzjlH2qM1IvsVY2YDgCVJq04F/gn4VbC+CNgCTHP3XVHVISLZaf/+/ZSUlHDgwAGqqqp48cUXgcSY9GXLltGjRw927NjBiBEjmDx5Ms888wwnn3wyv//97wHYvXs3hw8f5qabbmL58uUUFBSwZMkS7rzzThYvXtzocWtqahgxYgR33303t99+Ow8//DDf//73ufnmm5k3bx6jRo1i69atTJgwgU2bNh217UMPPUTXrl1Zv34969evZ+jQoQDs2LGDu+66ixdeeIFu3bpxzz33cN9993H77bdz4403smbNGk455RSmT59+1P4qKip4+eWX6dKlC9/85jcbPP7dd9/NhRdeyOLFi/nkk08YPnw4F110Ed26dWtx20cW/O7+DlACYGZ5wEfAMmABsMrdF5nZgmB5flR1iEh2Su7qWbt2Lddccw0bNmzA3bnjjjtYs2YNHTp04KOPPmLbtm0MHjyYW2+9lfnz5zNp0iS++tWvsmHDBjZs2MC4ceMAqKuro7CwMOVxO3XqdOSs+9xzz+X5558H4IUXXmDjxo1H3rdnzx4+/fRTunfvfmTdmjVrmDNnDgDFxcUUFxcD8Oqrr7Jx40bOP/98AA4dOsTIkSPZvHkzp5566pHx9dOnT6esrOzI/iZPnkyXLl1SHv+5555jxYoV3HvvvUBiKO7WrVs588wzm9XeydLVqTQWeN/dPzSzKcDoYH058BIKfpFYGzlyJDt27KC6upqVK1dSXV1NRUUF+fn5FBUVceDAAc444wwqKipYuXIlCxcuZPz48Vx22WUMGjSItWvXhj5Wfn7+keGPeXl51NYmumU/++wz1q5deySIG9PQ0El3Z9y4cTzxxBNHrX/jjTdS7iv5rL2x47s7Tz31FAMGDEi5r+ZIVx//VUB9i/R19yqA4LFPQxuY2SwzW2dm66qrq9NUpohkwubNm6mrq6NXr17s3r2bPn36kJ+fz+rVq/nww8TMwh9//DFdu3bl6quv5tZbb+X1119nwIABVFdXHwn+w4cP8/bbb7eohvHjx/PAAw8cWa7/ayTZBRdcwOOPPw7Ahg0bWL9+PQAjRozglVde4b333gNg3759vPvuuwwcOJAPPviALVu2ABz5XKI5x58wYQI///nPcXeg6V8mYUR+xm9mnYDJwMLmbOfuZUAZQGlpqUdQmogEup3QkdljTmvT/TWlvo8fEme15eXl5OXl8a1vfYtLLrmE0tJSSkpKGDhwIABvvfUWt912Gx06dCA/P5+HHnqITp068eSTTzJnzhx2795NbW0tc+fOZdCgQc2u+f7772f27NkUFxdTW1vLBRdcwC9/+cuj3vOd73yH6667juLiYkpKShg+fDgABQUFPPbYY0yfPp2DBw8CcNddd3HGGWfwi1/8gokTJ9K7d+8j72/O8X/wgx8wd+5ciouLcXeKiop4+umnm/39JbP63yJRCbp2Zrv7+GD5HWC0u1eZWSHwkrun/BumtLTUs+1GLJW79qUc/jZ7zGn069k1jRWJhLdp06ZW9RFLeHv37uXEE0/E3Zk9ezann3468+bNa/PjNPRvamYV7n7cRQbp6OqZzufdPAArgBnB8xlA68cmiYhkqYcffpiSkhIGDRrE7t27ufHGGzNdUrRdPWbWFRgHJH+ni4ClZjYT2ApMjbIGEZFMmjdvXiRn+K0RafC7+z6g1zHrdpIY5SMiGeTumqgtRzS3y15X7orEUOfOndm5c2ezA0OyT/18/J07dw69jebqEYmhfv36UVlZiYZK54b6O3CFpeAXiaH8/PzQd2uS3KOuHhGRmFHwi4jEjIJfRCRmFPwiIjGj4BcRiRkFv4hIzCj4RURiRsEvIhIzCn4RkZhR8IuIxIyCX0QkZhT8IiIxo0naUti17xA1B2sbfO1wnaazFZH2ScGfQs3B2kbvqztzVFF6ixERaSPq6hERiRkFv4hIzCj4RURiJtI+fjP7AvAIcDbgwPXAO8ASoAjYAkxz911R1pEplbv2Nbi+2wkd6dm1U5qrERFJiPrD3Z8Bz7j7FWbWCegK3AGscvdFZrYAWADMj7iOtDtwuI5HX97S4Guzx5ym4BeRjImsq8fMegAXAI8CuPshd/8EmAKUB28rBy6NqgYRETlelH38pwLVwL+b2Rtm9oiZdQP6unsVQPDYp6GNzWyWma0zs3XV1dURlikiEi9RBn9HYCjwkLufA9SQ6NYJxd3L3L3U3UsLCgqiqlFEJHaiDP5KoNLdXwuWnyTxi2CbmRUCBI/bI6xBRESOEVnwu/v/AX81swHBqrHARmAFMCNYNwNYHlUNIiJyvKhH9dwEPB6M6PkAuI7EL5ulZjYT2ApMjbgGERFJEmnwu/ubQGkDL42N8rgiItI4XbkrIhIzCn4RkZhR8IuIxIzm489CqW4Ao3l+RKS1FPxZKNUNYDTPj4i0lrp6RERiRsEvIhIzCn4RkZhR8IuIxIyCX0QkZhT8IiIxo+AXEYkZBb+ISMwo+EVEYkbBLyISMwp+EZGYUfCLiMSMgl9EJGYU/CIiMaPgFxGJmUjn4zezLcCnQB1Q6+6lZnYSsAQoArYA09x9V5R1iIjI59Jxxj/G3UvcvTRYXgCscvfTgVXBsoiIpEkmunqmAOXB83Lg0gzUICISW1EHvwPPmVmFmc0K1vV19yqA4LFPQxua2SwzW2dm66qrqyMuU0QkPqK+5+757v6xmfUBnjezzWE3dPcyoAygtLTUoypQRCRuIj3jd/ePg8ftwDJgOLDNzAoBgsftUdYgIiJHiyz4zaybmXWvfw6MBzYAK4AZwdtmAMujqkFERI4XZVdPX2CZmdUf5zfu/oyZ/RlYamYzga3A1AhrEBGRY0QW/O7+ATCkgfU7gbFRHVdERFLTlbsiIjGj4BcRiRkFv4hIzDQr+M2sg5n1iKoYERGJXpPBb2a/MbMewZDMjcA7ZnZb9KWJiEgUwpzxn+Xue0jMqbMS6A98O9KqREQkMmGCP9/M8kkE/3J3P0xiDh4REWmHwgT/v5GYN78bsMbMvgzsibIoERGJTpMXcLn7/cD9Sas+NLMx0ZUkIiJRCvPhbl8ze9TM/itYPovP59oREZF2JkxXz2PAs8DJwfK7wNyoChIRkWiFmaunt7svNbOFAO5ea2Z1EdeVNrv2HaLmYG2Drx2u02fYIpJ7wgR/jZn1IhjJY2YjgN2RVpVGNQdreXD1+w2+NnNUUXqLERFJgzDBfwuJOfRPM7NXgALgikirEhGRyIQZ1fO6mX0NGAAY8E4wll9ERNqhRoPfzP6hkZfOMDPc/bcR1SQiIhFKdcZ/SfDYBzgPeDFYHgO8BCj4RUTaoUaD392vAzCzp0nM11MVLBcCD6anPBERaWthxvEX1Yd+YBtwRkT1iIhIxMKM6nnJzJ4FniAxpPMqYHWkVYmISGTCjOr5rpldBlwQrCpz92VhD2BmecA64CN3n2RmJwFLgCISk79Nc/ddzS28vavcta/R13ThmIhEKcwZP8AfgVoSZ/x/auYxbgY2AfV37loArHL3RWa2IFie38x9tmsHDtfx6MtbGn1dF46JSJTCTNI2jUTYXwFMA14zs1AXcJlZP+Bi4JGk1VOA8uB5OYl5/kVEJE3CnPHfCQxz9+0AZlYAvAA8GWLbnwK3A92T1vWt/7DY3avMrE9DG5rZLGAWQP/+/UMcSkREwggzqqdDfegHdobZzswmAdvdvaIlhbl7mbuXuntpQUFBS3YhIiINCHPG/0zSqB6AK0nce7cp5wOTzewbQGegh5n9GthmZoXB2X4hsD3lXkREpE2FGdVzm5ldTiLIjZCjetx9IbAQwMxGA7e6+9Vm9hMSN3JZFDwub3n58ZRqRFC3EzrSs2unNFYjIu1NqFE97v4U8FQbHXMRsNTMZgJbgalttN9YaGpE0Owxpyn4RSSlJoM/mKztHhJz9ljw5e7eI+WGSdz9JRLz++DuO4GxLahVRETaQJgz/n8BLnH3TVEXIyIi0QszqmebQl9EJHeEOeNfZ2ZLgN8BB+tXaj5+EZH2KUzw9wD2AeOT1jmaj19EpF0KM5zzunQUIiIi6RGmj19ERHKIgl9EJGYU/CIiMRPmAq4vANeQuHHKkfe7+5zoyhIRkaiEGdWzEngVeAv4LNpyREQkamGCv7O73xJ5JSIikhZh+vj/w8z+0cwKzeyk+q/IKxMRkUiEOeM/BPyExJ246u8C7sCpURUlIiLRCRP8twBfcfcdURcjIiLRC9PV8zaJKRtERCQHhDnjrwPeNLPVHD1Jm4Zzioi0Q2GC/3fBl4iI5IAwk7SVp6MQERFJjzBX7v4vn4/mOcLdNapHRKQdCtPVU5r0vDOJm6M3OY7fzDoDa4ATguM86e4/DK4BWEJiCogtwDR339W8skVEpKWaHNXj7juTvj5y958CF4bY90HgQncfApQAE81sBLAAWOXupwOrgmUREUmTMF09Q5MWO5D4C6B7U9u5uwN7g8X84MuBKcDoYH058BIwP2zBIiLSOmG6ev416XktQfdMmJ2bWR5QAXwFeNDdXzOzvu5eBeDuVWbWp3kli4hIa4QZ1TOmpTt39zqgJJjaeZmZnR12WzObBcwC6N+/f0tLEBGRY4Tp6jkBuJzj5+P/cdiDuPsnZvYSMBHYZmaFwdl+IbC9kW3KgDKA0tLS40YViYhIy4SZsmE5iX75WqAm6SslMysIzvQxsy7ARcBmYAUwI3jbjGD/IiKSJmH6+Pu5+8QW7LsQKA/6+TsAS939aTNbCyw1s5nAVhLDQ0VEJE3CBP8fzWywu7/VnB27+3rgnAbW7wTGNmdfIiLSdsIE/yjg2uAK3oOAkRitWRxpZSIiEokwwf/1yKsQEZG0CTOc88N0FCIiIukR5oxfcsSufYeoOVjb4GvdTuhIz66d0lxR6pogc3WJ5DIFf4zUHKzlwdXvN/ja7DGnZSRgU9UEmatLJJeFGccvIiI5RGf80mrqrglH7STZQsEvrabumnDUTpIt1NUjIhIzCn4RkZhR8IuIxIyCX0QkZhT8IiIxo+AXEYkZBb+ISMxoHL+Ekurio8N10d4Zs3LXvgbX5+IFT3H6XiVzFPwSSqqLj2aOKorsuAcO1/Hoy1safC3XLniK0/cqmaWuHhGRmNEZv7Rr6hoRaT4Fv7Rb6hoRaZnIgt/MvgT8Cvg74DOgzN1/ZmYnAUuAImALMM3dd0VVh8RXY38NgP4ikHiL8oy/Fvieu79uZt2BCjN7HrgWWOXui8xsAbAAmB9hHRJDqf4aAP1FIPEW2Ye77l7l7q8Hzz8FNgFfBKYA5cHbyoFLo6pBRESOl5Y+fjMrAs4BXgP6unsVJH45mFmfRraZBcwC6N+/fzrKlAg11u0S9TUALaEbpkiuizz4zexE4ClgrrvvMbNQ27l7GVAGUFpamn3pIKGl6naJ8hqAltINUyTXRTqO38zySYT+4+7+22D1NjMrDF4vBLZHWYOIiBwtylE9BjwKbHL3+5JeWgHMABYFj8ujqkEkKrp+QNqzKLt6zge+DbxlZm8G6+4gEfhLzWwmsBWYGmENIm1O1w9IexdZ8Lv7y0BjHfpjozquiIikpit3c1BLR9CkuuApG0ffZKv2NIJJ4knBn2NaOoKmqQuesnH0TTZqbyOYJJ40O6eISMwo+EVEYkbBLyISMwp+EZGY0Ye7Ijkg1fxCuqhMjqXgF8kBqeYX0kVlcix19YiIxIyCX0QkZhT8IiIxo+AXEYkZfbgrEgO68bwkU/CL5DjdeF6OpeCX2NIsmhJXCn6JJc2iKXGmD3dFRGJGwS8iEjMKfhGRmFHwi4jETGQf7prZYmASsN3dzw7WnQQsAYqALcA0d98VVQ0iuSTb7omcakZQ0PUB2SzKUT2PAQ8Av0patwBY5e6LzGxBsDw/whpEckI23hM51YygoOsDsllkXT3uvgb42zGrpwDlwfNy4NKoji8iIg1Ldx9/X3evAgge+zT2RjObZWbrzGxddXV12goUEcl1WfvhrruXuXupu5cWFBRkuhwRkZyR7uDfZmaFAMHj9jQfX0Qk9tId/CuAGcHzGcDyNB9fRCT2Igt+M3sCWAsMMLNKM5sJLALGmdlfgHHBsoiIpFFkwzndfXojL42N6pgiItK0rP1wV0REoqHgFxGJGQW/iEjMKPhFRGJGwS8iEjMKfhGRmNE9d0Wk0SmfNbVyblLwi8RcqimfNbVyblLwi0ja6SYumaXgF5G0001cMksf7oqIxIzO+EUkpcY++A1zn9/WbCvRUfCLSKNSffDb1H1+W7OtREtdPSIiMaMzfhHJSrq2IDoKfhHJOrq2IFrq6hERiRkFv4hIzCj4RURiRsEvIhIzGflw18wmAj8D8oBH3H1RVMdqak4QXUgi0v40NuIHID/PGv25bs2IoFRZEtV+W7vvxqQ9+M0sD3gQGAdUAn82sxXuvjGK4zU1J4guJBFpX1KN+IHEz3QUI4JSZUlU+23tvhuTia6e4cB77v6Bux8C/hOYkoE6RERiydzT29VhZlcAE939hmD528Dfu/t3j3nfLGBWsDgAeKeFh+wN7GjhtnGidgpPbRWO2imcKNvpy+5ecOzKTPTxWwPrjvvt4+5lQFmrD2a2zt1LW7ufXKd2Ck9tFY7aKZxMtFMmunoqgS8lLfcDPs5AHSIisZSJ4P8zcLqZnWJmnYCrgBUZqENEJJbS3tXj7rVm9l3gWRLDORe7+9sRHrLV3UUxoXYKT20VjtopnLS3U9o/3BURkczSlbsiIjGj4BcRiZmcDn4zm2hm75jZe2a2INP1ZAszW2xm281sQ9K6k8zseTP7S/DYM5M1ZgMz+5KZrTazTWb2tpndHKxXWyUxs85m9icz+5+gnf45WK92aoCZ5ZnZG2b2dLCc9nbK2eBPmhri68BZwHQzOyuzVWWNx4CJx6xbAKxy99OBVcFy3NUC33P3M4ERwOzg/5Da6mgHgQvdfQhQAkw0sxGonRpzM7ApaTnt7ZSzwY+mhmiUu68B/nbM6ilAefC8HLg0rUVlIXevcvfXg+efkvhh/SJqq6N4wt5gMT/4ctROxzGzfsDFwCNJq9PeTrkc/F8E/pq0XBmsk4b1dfcqSAQe0CfD9WQVMysCzgFeQ211nKD74k1gO/C8u6udGvZT4Hbgs6R1aW+nXA7+UFNDiDTFzE4EngLmuvueTNeTjdy9zt1LSFyJP9zMzs50TdnGzCYB2929ItO15HLwa2qI5tlmZoUAweP2DNeTFcwsn0ToP+7uvw1Wq60a4e6fAC+R+AxJ7XS084HJZraFRNfzhWb2azLQTrkc/JoaonlWADOC5zOA5RmsJSuYmQGPApvc/b6kl9RWScyswMy+EDzvAlwEbEbtdBR3X+ju/dy9iEQevejuV5OBdsrpK3fN7Bsk+tTqp4a4O8MlZQUzewIYTWI62G3AD4HfAUuB/sBWYKq7H/sBcKyY2Sjgv4G3+LxP9g4S/fxqq4CZFZP4UDKPxMnkUnf/sZn1Qu3UIDMbDdzq7pMy0U45HfwiInK8XO7qERGRBij4RURiRsEvIhIzCn4RkZhR8IuIxIyCX0QkZhT8IiIx8//qTKcET5DhHwAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "G_baseline, cohorts, teams = generate_workplace_contact_network(\n",
+    "                                 num_cohorts=NUM_COHORTS, num_nodes_per_cohort=NUM_NODES_PER_COHORT, \n",
+    "                                 num_teams_per_cohort=NUM_TEAMS_PER_COHORT,\n",
+    "                                 mean_intracohort_degree=MEAN_INTRACOHORT_DEGREE, \n",
+    "                                 pct_contacts_intercohort=PCT_CONTACTS_INTERCOHORT,\n",
+    "                                 farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10, \n",
+    "                                              'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False})\n",
+    "\n",
+    "network_info(G_baseline, \"Baseline\", plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define the quarantine contact network to be an empty network (i.e., no connections). This represents an assumption that an employee that is in a quarantine state makes no contact with anyone from their workplace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_quarantine = networkx.classes.function.create_empty_copy(G_baseline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Specifying parameters\n",
+    "\n",
+    "**_The parameter values used in this notebook reflect rough estimates of parameter values for the COVID-19 epidemic (as of 9 Aug 2020)._**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set disease progression rate parameters:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parameter values are assigned to members of the population on an individual basis. Parameter values can be [specified to the `ExtSEIRSNetworkModel`](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#specifying-parameters) by providing a list of values that gives the *N* values to assign to each individual. The population may be either homogeneous or heterogeneous for a given parameter at the user's discretion. \n",
+    "\n",
+    "**Here we generate distributions of values for each parameter, thus specifying a realistically heterogeneous population.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected latent periods (time in Exposed state) and presymptomatic periods (time in Pre-symptomatic infectious state). The `sigma` and `lamda` rates are calculated as the inverse of the expected exposed and pre-symptomatic periods assigned to each individual, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "latent period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
+      "\n",
+      "pre-symptomatic period:  mean = 2.94, std = 1.72, 95% CI = (0.64, 7.12)\n",
+      "\n",
+      "total incubation period:  mean = 5.14, std = 2.03, 95% CI = (2.17, 10.02)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latentPeriod_mean, latentPeriod_coeffvar = 3.0, 0.6\n",
+    "SIGMA   = 1 / gamma_dist(latentPeriod_mean, latentPeriod_coeffvar, N)\n",
+    "\n",
+    "presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar = 2.2, 0.5\n",
+    "LAMDA   = 1 / gamma_dist(presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/LAMDA, 1/SIGMA, 1/LAMDA+1/SIGMA], [\"latent period\", \"pre-symptomatic period\", \"total incubation period\"], plot=True, colors=['gold', 'darkorange', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected (a)symptomatic periods (time in symptomatic or asymptomatic state). The `gamma` rates are calculated as the inverse of the expected (a)symptomatic periods assigned to each individual. \n",
+    "\n",
+    "The expected total infectious period for each individual is the sum of their expected pre-symptomatic and (a)symptomatic periods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pre-symptomatic period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
+      "\n",
+      "(a)symptomatic period:  mean = 4.01, std = 1.54, 95% CI = (1.64, 7.36)\n",
+      "\n",
+      "total infectious period:  mean = 6.20, std = 1.87, 95% CI = (3.09, 10.45)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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aZJ9adKQQbTpSkAhUhSMFqR50pCAiIidNRUFERAIqCiIiElBREBGRgIqCSBWQl5fH1q1bo3bLy8urcH979+5l/Pjxx80r0vbVubm5ZGRklFq+c+dObrjhhuNu/9JLL9GqVSu6det23HWPNXnyZHbu3Bk8vuOOO45qhV3VVLVW2cfSyWsiVcD+/fuPahD3bY0YMYLU1NRyny8uCj/96U8rjPNt21efc845R50QVp5nn32W8ePHn3RRyMjI4JxzzgHgr3/96wnHqCxVsVX2sXSkIFINDR8+nC1btpCVlcWwYcNwd4YNG0ZGRgaZmZnMnDkzWK9k++rc3FwuvfRS2rVrR7t27Xj//fcr3E/JI4jJkydz3XXX0aNHD5o3b86vfvUrIHT9gsWLFzNkyBCGDRtGYWEhw4YNo3379vzgBz/gL3/5SxDvySefJDMzkzZt2jB8+HBefvllli1bRr9+/cjKyuLgwYN07dqV4qnqM2bMIDMzk4yMDB588MEgTsmzkl9++WUGDhwIhI5YMjIyaNOmDV26dCn1+5yKrbKPpSMFkWpo9OjRrFmzhpycHABeeeWVoB32nj17aN++PV26dGH06NFHnbF74MAB3nnnHZKTk9m0aRN9+/blRM4VysnJYcWKFdSpU4cLL7yQe++9l4cffph3332XMWPGkJ2dzcSJE2nQoAFLly7l8OHDdOrUiSuuuIL169cza9YsPvroI+rVq8eXX35Jw4YNefrpp4NtS9q5cycPPvggH3/8MWeeeSZXXHEFs2bNonfv3uXmN2rUKN5++23OPfdc9u7dW+Y6S5YsYe3atZx//vn06NGDV199la5duwatsk877TR+97vfMXbsWB5++GHgP62yIfQPH/7TKnv+/Pm0aNGCW2+9lQkTJjBkyBAGDx7Mu+++y/e+970yO6rGko4URITFixfTt29fatasSePGjbnsssuCRnAlFRQUMHjwYDIzM7nxxhtPeOy+e/fuNGjQgOTkZFq3bn1U471ic+fO5YUXXiArK4uOHTuSl5fHpk2bmDdvHoMGDaJevXoANGzYsMJ9LV26lK5duwY9lPr168eiRYsq3KZTp04MHDiQSZMmlXumd3Gr7Jo1awatsj/88MOgVXZWVhZTpkw56neLtFX2okWLWL9+fdAq28y45ZZbKsw52nSkICIVtm0uady4cTRu3JiVK1dSVFREcnLyCe2nZAO7mjVrltl11N35n//5n1LXO5gzZ84JtZCOtBV1yRbhf/7zn/noo4948803ycrKIicnp9R3M6daq+xj6UghkXy1Nfq3gxXPUpFTU1ltqWfOnElhYSG7d+9m0aJFdOjQodR6X331FU2aNKFGjRpMnTq1wr5JJ+vKK69kwoQJwZXcNm7cyDfffMMVV1zBc889x4EDBwCCy2yW1/q6Y8eO/P3vf2fPnj0UFhYyY8YMLrvsMgAaN27MunXrKCoq4rXXXgu22bJlCx07dmTUqFE0atSI7du3l4p7qrXKPlbMjhTM7DzgBeBsoAiY6O5/NLORwGBgd3jVEe7+Vnibh4DbgULg5+7+dqzySzhHDsLycdGP22EE1C1/lopUjpSUFEaMGBHVeBVJTU2lU6dOZGRk8OMf/5gnn3ySDz74gDZt2mBmPPnkk5x99tmkpqaWal99/fXX89JLL9GtW7cyPwF/W3fccQe5ubm0a9cOdyctLY1Zs2bRo0cPcnJyyM7OJikpiauuuoonnniCgQMHMmTIEOrWrcsHH3wQxGnSpAm//e1v6datG+7OVVddFVyTevTo0fTs2ZPzzjuPjIyM4EvhYcOGsWnTJtyd7t2706ZNm1L5nWqtso8Vs4Z4ZtYEaOLuy82sPvAx0Bu4Cdjv7mOOWb81MAPoAJwDzANauHu5H0WqVUO8dkNjVxTUaK/SqSFeYkqEVtnHqjIN8dx9l7svD9/fB6wDzq1gk17A39z9sLt/CmwmVCBERKSSVMp3CmaWDrQFPgovusfMVpnZc2ZWfJmmc4GSA3g7KKOImNmdZrbMzJbt3r372KdFRGKma9euCXWUcDJiXhTMLAV4Bbjf3b8GJgAXAFnALuD3xauWsXmpsS13n+ju2e6enZaWFqOsRWIvka9lIonhZN5jMS0KZlabUEGY5u6vArj75+5e6O5FwCT+M0S0AzivxOZNgZ2InIKSk5PJy8tTYZCYcXfy8vJOeNpwLGcfGfAssM7dx5ZY3sTdd4UfXgsUf60+G5huZmMJfdHcHFgSq/xE4qlp06bs2LEDDYFKLCUnJ9O0adMT2iaWJ691AvoDq80sJ7xsBNDXzLIIDQ3lAncBuPsnZvYisBY4AvysoplHIomsdu3aNGvWLN5piJQSs6Lg7osp+3uCtyrY5nHg8VjlJCIiFdMZzSIiElBREBGRgIqCiIgEVBRERCSgoiAiIgEVBRERCagoiIhIQEVBREQCKgoiIhJQURARkYCKgoiIBFQUREQkoKIgIiIBFQUREQmoKIiISEBFQUREAioKIiISUFEQEZGAioKIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAIxKwpmdp6ZLTCzdWb2iZndF17e0MzeMbNN4Z9nltjmITPbbGYbzOzKWOUmIiJli+WRwhHgAXdvBVwE/MzMWgPDgfnu3hyYH35M+Lk+wPeBHsB4M6sZw/xEROQYMSsK7r7L3ZeH7+8D1gHnAr2AKeHVpgC9w/d7AX9z98Pu/imwGegQq/xERKS0EyoKZlbDzE4/0Z2YWTrQFvgIaOzuuyBUOICzwqudC2wvsdmO8LJjY91pZsvMbNnu3btPNBUREanAcYuCmU03s9PN7DRgLbDBzIZFugMzSwFeAe53968rWrWMZV5qgftEd8929+y0tLRI0xARkQjUimCd1u7+tZn1A94CHgQ+Bp463oZmVptQQZjm7q+GF39uZk3cfZeZNQG+CC/fAZxXYvOmwM4Ifw85SUVHCijc/q+ox62RUpeaZzaIelwRia1IikLt8D/33sDT7l5gZqU+wR/LzAx4Fljn7mNLPDUbGACMDv98vcTy6WY2FjgHaA4sifg3kZNzpIi9f5ga9bBn3N9fRUEkAUVSFP4C5AIrgUVmdj5Q0TBQsU5Af2C1meWEl40gVAxeNLPbgW3AjQDu/omZvUhoiOoI8DN3LzyB30VERL6l4xYFd/8T8KcSi7aaWbcItltM2d8TAHQvZ5vHgcePF1tERGIjki+aG5vZs2b2/8KPWxMa9hERkVNMJFNSJwNvExrnB9gI3B+rhEREJH4iKQqN3P1FoAjA3Y8AGusXETkFRVIUvjGzVMLnDJjZRcBXMc1KRETiIpLZR78gNF30AjP7B5AG3BDTrEREJC4imX203MwuAy4kNJtog7sXxDwzSXgFUT4pTifEicReuUXBzK4r56kWZkaJM5RFSik6dJiv//xiVGPqhDiR2KvoSOHq8M+zgEuAd8OPuwELARUFEZFTTLlFwd0HAZjZG4T6H+0KP24CPFM56Ukx9yKK9h+IQeCi6McUkYQVyRfN6cUFIexzoEWM8pHyOOSv3hT1sEkXRT2kiCSwSIrCQjN7G5hBaFpqH2BBTLMSEZG4iGT20T1mdi3QJbxooru/Ftu0REQkHiI5UgB4n1DnUkftrEVETlmRNMS7iVAhuAG4CfjIzHTymojIKSiSI4VfA+3d/QsAM0sD5gEvxzIxERGpfJH0PqpRXBDC8iLcTkREEkwkRwpzSsw+AriZ0LWaRUTkFBPJ7KNhZnY9octrGpp9JKeYvLw89u/fH/W4KSkppKamRj2uSCxFNPvI3V8BXolxLiIVKsjPZ+fWrVGPm5+fz5gxY6Ied8SIESoKknCOWxTCjfF+R6gHkoVv7u6nxzg3kaMcKSzkiSdHRz3u0KFDox5TJFFFcqTwJHC1u6+LdTIiIhJfkcwi+lwFQUSkeojkSGGZmc0EZgGHixfqegoiIqeeSIrC6cAB4IoSyxxdT0FE5JQTyZTUQZWRiIiIxF/Mzkw2s+fM7AszW1Ni2Ugz+8zMcsK3q0o895CZbTazDWZ2ZazyEhGR8sWyXcVkoEcZy8e5e1b49haAmbUmdJ2G74e3GW9mNWOYm4iIlCFmRcHdFwFfRrh6L+Bv7n7Y3T8FNgMdYpWbiIiULZKT184AbgXSS67v7j8/yX3eY2a3AsuAB9z938C5wIcl1tkRXlZWPncCdwJ85zvfOckURESkLJEcKbxFqCCsBj4ucTsZE4ALgCxgF/D78HIrY10vK4C7T3T3bHfPTktLO8k0RESkLJFMSU12919EY2fu/nnxfTObBLwRfrgDOK/Eqk2BndHYp4iIRC6SI4WpZjbYzJqYWcPi28nszMyalHh4LVA8M2k20MfM6phZM6A5uuyniEili+RIIR94itAV2IqHdBz4bkUbmdkMoCvQyMx2AI8AXc0sK7x9LnAXgLt/YmYvAmsJXQv6Z+5eeKK/jIiIfDuRFIVfAN9z9z0nEtjd+5ax+NkK1n8cePxE9iEiItEVyfDRJ4TaXIiIyCkukiOFQiDHzBZwdEO8k52SKtWAFxWxb9++qMZM8qKoxhOR0iIpCrPCN5GIucOKFSuiGrO7D4hqPBEpLZKGeFMqIxEREYm/SM5o/pQyTiRz9wpnH4mISOKJZPgou8T9ZOBG4KTOUxARkartuLOP3D2vxO0zd/8DcHkl5CYiIpUskuGjdiUe1iB05FA/ZhmJiEjcRDJ89PsS948QOhP5pphkIyIicRXJ7KNulZGIiIjEXyTDR3WA6yl9PYVRsUtLRETiIZLho9eBrwhdQ+HwcdYVEZEEFklRaOruZV1rWURETjGRNMR738wyY56JiIjEXSRHCp2BgeEzmw8TunSmu/sPYpqZiIhUukiKwo9jnoWIiFQJkUxJ3VoZiYiISPxFcqQgpzBLqs3pN7ePetyaDSzqMRPR1q3R/UyVkpJCampqVGOKlKSiUN0VHqJozvCoh611x/NRj5loDh48yLhx46Iac8SIESoKElORzD4SEZFqQkVBREQCKgoiIhJQURARkYCKgoiIBGI2+8jMngN6Al+4e0Z4WUNgJqGOq7nATe7+7/BzDwG3A4XAz9397VjlJokpKSmJu6+N/qU8GiXVjXpMkUQVyympk4GngRdKLBsOzHf30WY2PPz4QTNrDfQBvg+cA8wzsxbuXhjD/CTBWH4BW37zh6jHTZ8a/ZgiiSpmw0fuvgj48pjFvYAp4ftTgN4llv/N3Q+7+6fAZqBDrHITEZGyVfZ3Co3dfRdA+OdZ4eXnAttLrLcjvExERCpRVfmiuayeCF7mimZ3mtkyM1u2e/fuGKclIlK9VHZR+NzMmgCEf34RXr4DOK/Eek2BnWUFcPeJ7p7t7tlpaWkxTVZEpLqp7KIwGxgQvj+A0KU+i5f3MbM6ZtYMaA4sqeTcRESqvVhOSZ0BdAUamdkO4BFgNPCimd0ObANuBHD3T8zsRWAtcAT4mWYeiYhUvpgVBXfvW85T3ctZ/3Hg8VjlIyIix1dVvmgWEZEqQEVBREQCKgoiIhJQURARkYCKgoiIBFQUREQkoKIgIiIBFQUREQmoKIiISEBFQUREAioKIiISiOXlOKu+g3mQvz+6MYvyoxtPRKQSVe+ikL8fljwR3ZjthkY3nohIJdLwkYiIBFQUREQkUL2Hj0QS0NatW6MeMyUlhdTU1KjHlcSjoiCSQA4ePMi4ceOiHnfEiBEqCgKoKAhQUFAQ9ZhJUY8oIpVBRUHYs2dP1GOeFvWIIlIZ9EWziIgEVBRERCSgoiAiIgEVBRERCagoiIhIQEVBREQCcZmSama5wD6gEDji7tlm1hCYCaQDucBN7v7veOQnIlJdxfNIoZu7Z7l7dvjxcGC+uzcH5ocfi4hIJapKw0e9gCnh+1OA3nHMRUSkWorXGc0OzDUzB/7i7hOBxu6+C8Ddd5nZWWVtaGZ3AncCfOc736msfCPmXkTR/gNRj2t41GOKiBwrXkWhk7vvDP/jf8fM1ke6YbiATATIzs6uev8pHfJXb4p62Dqdoh5SRKSUuBQFd98Z/vmFmb0GdAA+N7Mm4aOEJsAX8chNqp+kpCTuvvamqMdtlFQ36jFFYq3Si4KZnQbUcPd94ftXAKOA2cAAYIPiNpwAAAZwSURBVHT45+uVnZtUT5ZfwJbf/CHqcdOnRj+mSKzF40ihMfCamRXvf7q7zzGzpcCLZnY7sA24MQ65iYhUa5VeFNz9n0CbMpbnAd0rOx8REfmPqjQlVURE4kxFQUREArrymsSEJSXR6dFBUY1Zu4E+w4jEmoqCxEbRIQ68PjSqIe0Hc6IaT0RK00cvEREJqCiIiEhARUFERAIqCiIiElBREBGRgGYfiQgAW7dujXrMlJQUUlNTox5XYkdFQUQ4ePAg48aNi3rcESNGqCgkGA0fiYhIQEcKIjESi+s06BoNEmsqCiIxEovrNOgaDRJrGj4SEZGAioKIiAQ0fJRACgoKoh6zTtQjihxNU10Ti4pCAtmzZ0/UY6ZEPaLIf2iqa+LR8JGIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAKakioJw5KS6PTooKjHrd0gcT4bxaKfEqinEkBeXh779++PetxEO6eiyhUFM+sB/BGoCfzV3UfHOSWpKooOceD1oVEPaz+YE/WYsRKLfkoALWaOT7hiE+2T4vLz8xkzZkxUY0LinVNRpYqCmdUEngF+BOwAlprZbHdfG9/MRE5tsSo2sWrgF4uT4oYOjf4HjmKJdFZ3lSoKQAdgs7v/E8DM/gb0AmJSFIqOFOD7D0Q1prmrHYWIBBLtrG5z96gHPVlmdgPQw93vCD/uD3R093tKrHMncGf44YXAhm+xy0ZA9HtHxEYi5QqJla9yjZ1EyjeRcoVvl+/57p5W1hNV7UjBylh2VNVy94nAxKjszGyZu2dHI1asJVKukFj5KtfYSaR8EylXiF2+VW3axQ7gvBKPmwI745SLiEi1U9WKwlKguZk1M7MkoA8wO845iYhUG1Vq+Mjdj5jZPcDbhKakPufun8Rwl1EZhqokiZQrJFa+yjV2EinfRMoVYpRvlfqiWURE4quqDR+JiEgcqSiIiEigWhYFM+thZhvMbLOZDY93PhUxs/PMbIGZrTOzT8zsvnjndDxmVtPMVpjZG/HO5XjM7Awze9nM1odf44vjnVN5zGxo+D2wxsxmmFlyvHMqycyeM7MvzGxNiWUNzewdM9sU/nlmPHMsVk6uT4XfB6vM7DUzOyOeOZZUVr4lnvulmbmZNYrGvqpdUSjRSuPHQGugr5m1jm9WFToCPODurYCLgJ9V8XwB7gPWxTuJCP0RmOPuLYE2VNG8zexc4OdAtrtnEJqI0Se+WZUyGehxzLLhwHx3bw7MDz+uCiZTOtd3gAx3/wGwEXiospOqwGRK54uZnUeoLdC2aO2o2hUFSrTScPd8oLiVRpXk7rvcfXn4/j5C/7TOjW9W5TOzpsBPgL/GO5fjMbPTgS7AswDunu/ue+ObVYVqAXXNrBZQjyp2Do+7LwK+PGZxL2BK+P4UoHelJlWOsnJ197nufiT88ENC50lVCeW8tgDjgF9xzEm+30Z1LArnAttLPN5BFf4nW5KZpQNtgY/im0mF/kDoTVoU70Qi8F1gN/B8eLjrr2Z2WryTKou7fwaMIfSJcBfwlbvPjW9WEWns7rsg9AEHOCvO+UTqNuD/xTuJipjZNcBn7r4ymnGrY1E4biuNqsjMUoBXgPvd/et451MWM+sJfOHuH8c7lwjVAtoBE9y9LfANVWd44yjhsfheQDPgHOA0M7slvlmdmszs14SGbafFO5fymFk94NfAw9GOXR2LQsK10jCz2oQKwjR3fzXe+VSgE3CNmeUSGpa73Mz+b3xTqtAOYIe7Fx95vUyoSFRFPwQ+dffd7l4AvApcEuecIvG5mTUBCP/8Is75VMjMBgA9gX5etU/iuoDQB4SV4b+3psByMzv72waujkUhoVppmJkRGvNe5+5j451PRdz9IXdv6u7phF7Xd929yn6adfd/AdvN7MLwou7EqE17FGwDLjKzeuH3RHeq6Jfix5gNDAjfHwC8HsdcKhS+wNeDwDXuHt2e+lHm7qvd/Sx3Tw//ve0A2oXf099KtSsK4S+SiltprANejHErjW+rE9Cf0KfunPDtqngndQq5F5hmZquALOCJOOdTpvDRzMvAcmA1ob/dKtWWwcxmAB8AF5rZDjO7HRgN/MjMNhGaJVMlrqRYTq5PA/WBd8J/Z3+Oa5IllJNvbPZVtY+QRESkMlW7IwURESmfioKIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAL/H/Fx2N8iHDi3AAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "symptomaticPeriod_mean, symptomaticPeriod_coeffvar = 4.0, 0.4\n",
+    "GAMMA   = 1 / gamma_dist(symptomaticPeriod_mean, symptomaticPeriod_coeffvar, N)\n",
+    "\n",
+    "infectiousPeriod = 1/LAMDA + 1/GAMMA\n",
+    "\n",
+    "dist_info([1/LAMDA, 1/GAMMA, 1/LAMDA+1/GAMMA], [\"pre-symptomatic period\", \"(a)symptomatic period\", \"total infectious period\"], plot=True, colors=['darkorange', 'crimson', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of expected onset-to-hospitalization periods (time in symptomatic state before entering hospitalized state for those with severe cases) and hospitalization-to-discharge periods (time in hospitalized state for those with non-fatal cases). The `eta` and `gamma_H` rates are calculated as the inverse of the expected onset-to-hospitalization periods and hospitalization-to-discharge periods assigned to each individual, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
+      "\n",
+      "hospitalization-to-discharge period:  mean = 11.38, std = 5.29, 95% CI = (3.49, 23.54)\n",
+      "\n",
+      "onset-to-discharge period:  mean = 22.51, std = 7.16, 95% CI = (10.62, 39.43)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar = 11.0, 0.45\n",
+    "ETA     = 1 / gamma_dist(onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar, N)\n",
+    "\n",
+    "hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar = 11.0, 0.45\n",
+    "GAMMA_H = 1 / gamma_dist(hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/ETA, 1/GAMMA_H, 1/ETA+1/GAMMA_H], [\"onset-to-hospitalization period\", \"hospitalization-to-discharge period\", \"onset-to-discharge period\"], plot=True, colors=['crimson', 'violet', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate a distribution of hospitalization-to-death periods (time in hospitalized state for those with fatal cases). The `mu_H` rates are calculated as the inverse of the expected hospitalization-to-death periods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
+      "\n",
+      "hospitalization-to-death period:  mean = 6.87, std = 3.10, 95% CI = (2.01, 13.61)\n",
+      "\n",
+      "onset-to-death period:  mean = 17.99, std = 6.08, 95% CI = (8.34, 31.59)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar = 7.0, 0.45\n",
+    "MU_H    = 1 / gamma_dist(hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar, N)\n",
+    "\n",
+    "dist_info([1/ETA, 1/MU_H, 1/ETA+1/MU_H], [\"onset-to-hospitalization period\", \"hospitalization-to-death period\", \"onset-to-death period\"], plot=True, colors=['crimson', 'darkgray', 'black'], reverse_plot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set severity parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specify the percentage of cases that are asymptomatic. This percentage of case will progress from the pre-symptomatic state to the asymptomatic state, rather than to the symptomatic state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_ASYMPTOMATIC = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the case hospitalization rate. The value used here is approximately the age-frequency-weighted average of age-stratified hospitalization rates for working age adults using data from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_HOSPITALIZED = 0.035"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the case fatality rate for hospitalized cases. The value used here is approximately the age-frequency-weighted average of age stratified hospitalization fatality rates for working age adults, again using figures from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PCT_FATALITY = 0.08"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set transmission parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#stochastic-network-model-implementation) model considers two modes of disease transmission: a well-mixed mode of [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) and a contact network based mode of [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The propensity for a given individual to become exposed due to global transmission depends on the mean transmissibility of all infectious individuals in the population; the propensity for a given individual to become exposed due to local transmission depends on the pairwise transmissibilities between the focal node and its infectious contacts in the network (see [Transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#transmission) and [Model Equations](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#model-equations) for more information about these calculations). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The transmissibility parameter *β* can be related to the basic reproduction number *R<sub>0</sub>* (i.e., the expected number of new infections generated by a single infectious individual in a completely susceptible population) by the standard formula: *β = R<sub>0</sub>𝛾*. *R<sub>0</sub>* is a more interpretable parameter, so we specify transmissibility in terms of *R<sub>0</sub>* and then calculate the corresponding *β* values.\n",
+    "\n",
+    "First, we generate a distribution of individual *R<sub>0</sub>* values (i.e., the expected number of new infections generated by a single *particular* infectious individual in a completely susceptible population). Of course, this means that transmissibility is heterogeneous in this population. The coefficient of variation is an important parameter for the individual *R<sub>0</sub>* distribution in that it tunes the degree of superspreading in the heterogeneous transmissibility. The distribution used in this example has a relatively low coefficient of variation, so most individuals have around the same degree of transmissibility. But a higher coefficient of variation (e.g., 2.0) would give a long right tail in idividual transmissibility representing a small number of individuals contributing many cases while the majority cases contribute less than 1 on average when they are infectious."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Individual R0:  mean = 1.98, std = 0.41, 95% CI = (1.32, 2.89)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "R0_mean     = 2.0\n",
+    "R0_coeffvar = 0.2\n",
+    "\n",
+    "R0 = gamma_dist(R0_mean, R0_coeffvar, N)\n",
+    "\n",
+    "dist_info(R0, \"Individual R0\", bin_size=0.1, plot=True, colors='crimson')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Individuals are ultimately assigned an [*Individual Transmissibility Value*](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#transmissibility-parameters) (*β<sup>i</sup>*), which are stored in the `beta` attribute of the model object. \n",
+    "\n",
+    "The means of the Individual Transmissibility Values for infectious subpopulations are used to calculate the [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) terms. Individual Transmissibility Values may also be used to generate the Pairwise Transmissibility Values used for [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission) terms, as we will specify in a few steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BETA = 1/infectiousPeriod * R0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the stochastic network model, an individual comes into contact with a random individual from the population at large (e.g., in a public space) with probability *p* or with an individual from their set of close contacts with probability *(1-p)*. Transmission that occurs between an individual and the population at large is referred to as [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission), and transmission between an individual and one of their close contacts (network neighbors) is referred to as [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The parameter *p* defines the locality of the network: for *p=0* an individual only interacts with their close contacts, while *p=1* represents a uniformly mixed population.\n",
+    "\n",
+    "Here we set *p* to reflect 40% of interactions being with incidental or casual contacts outside their set of close contacts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P_GLOBALINTXN = 0.4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Testing, Tracing, & Isolation (TTI) intervention protocol parameters:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we specify the parameters that govern the Testing, Tracing, and Isolation protocol that is implemented by the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). The implementation of this TTI protocol and the interpretation of these parameters is desribed in detail on the [TTI Simulation Loop wiki page](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) (but these parameters are briefly explained as code comments below).\n",
+    "\n",
+    "**The scenario set up in the steps that follow involves the entire workforce being tested on a weekly basis, a 2-day test turn around time, 50% of symptomatic individuals self-reporting and getting tested within 1 day of onset, 30% of symptomatics self-isolating even without a positive test, and teams of detected positive cases being proactively isolated. A new exogenous exposures comes into the workplace about once a week.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INTERVENTION_START_PCT_INFECTED = 0/100\n",
+    "AVERAGE_INTRODUCTIONS_PER_DAY   = 1/14          # expected number of new exogenous exposures per day\n",
+    "\n",
+    "TESTING_CADENCE                 = 'weekly'      # how often to do testing (other than self-reporting symptomatics who can get tested any day)\n",
+    "PCT_TESTED_PER_DAY              = 1.0           # max daily test allotment defined as a percent of population size\n",
+    "TEST_FALSENEG_RATE              = 'temporal'    # test false negative rate, will use FN rate that varies with disease time\n",
+    "MAX_PCT_TESTS_FOR_SYMPTOMATICS  = 1.0           # max percent of daily test allotment to use on self-reporting symptomatics\n",
+    "MAX_PCT_TESTS_FOR_TRACES        = 0.0           # max percent of daily test allotment to use on contact traces\n",
+    "RANDOM_TESTING_DEGREE_BIAS      = 0             # magnitude of degree bias in random selections for testing, none here\n",
+    "\n",
+    "PCT_CONTACTS_TO_TRACE           = 0.0           # percentage of primary cases' contacts that are traced\n",
+    "TRACING_LAG                     = 2             # number of cadence testing days between primary tests and tracing tests\n",
+    "\n",
+    "ISOLATION_LAG_SYMPTOMATIC       = 1             # number of days between onset of symptoms and self-isolation of symptomatics\n",
+    "ISOLATION_LAG_POSITIVE          = 2             # test turn-around time (TAT): number of days between administration of test and isolation of positive cases\n",
+    "ISOLATION_LAG_CONTACT           = 0             # number of days between a contact being traced and that contact self-isolating\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set intervention compliance parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we specify the compliance rates (i.e., the percentage of individuals who are compliant) for each intervention type. See the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) documentation for more information about compliance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TESTING_COMPLIANCE_RATE_SYMPTOMATIC                  = 0.5    \n",
+    "TESTING_COMPLIANCE_RATE_TRACED                       = 0.0\n",
+    "TESTING_COMPLIANCE_RATE_RANDOM                       = 1.0  # Assume employee testing is mandatory, so 100% compliance\n",
+    "\n",
+    "TRACING_COMPLIANCE_RATE                              = 0.0\n",
+    "\n",
+    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL     = 0.3\n",
+    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE      = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL        = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE         = 0.8  # Isolate teams with a positive member, but suppose 20% of employees are essential workforce\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT           = 0.0\n",
+    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE  = 0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we randomly assign a `True/False` compliance to each individual according to the rates set above. Individuals whose compliance is set to `True` for a given intervention will participate in that intervention, individuals set to `False` will not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TESTING_COMPLIANCE_RANDOM                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_RANDOM)\n",
+    "TESTING_COMPLIANCE_TRACED                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_TRACED)\n",
+    "TESTING_COMPLIANCE_SYMPTOMATIC                   = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_SYMPTOMATIC)\n",
+    "\n",
+    "TRACING_COMPLIANCE                               = (numpy.random.rand(N) < TRACING_COMPLIANCE_RATE)\n",
+    "\n",
+    "ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL      = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL)\n",
+    "ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE       = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL         = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE          = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_CONTACT            = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT)\n",
+    "ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE   = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\boaz\\PycharmProjects\\seirsplus\\seirsplus\\models.py:2143: RuntimeWarning: invalid value encountered in true_divide\n",
+      "  self.delta_Q             = numpy.log(self.degree_Q)/numpy.log(numpy.mean(self.degree_Q)) if self.parameters['delta_Q'] is None else numpy.array(self.parameters['delta_Q']) if isinstance(self.parameters['delta_Q'], (list, numpy.ndarray)) else numpy.full(fill_value=self.parameters['delta_Q'], shape=(self.numNodes,1))\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ExtSEIRSNetworkModel(G=G_baseline, p=P_GLOBALINTXN,\n",
+    "                              beta=BETA, sigma=SIGMA, lamda=LAMDA, gamma=GAMMA, \n",
+    "                              gamma_asym=GAMMA, eta=ETA, gamma_H=GAMMA_H, mu_H=MU_H, \n",
+    "                              a=PCT_ASYMPTOMATIC, h=PCT_HOSPITALIZED, f=PCT_FATALITY,              \n",
+    "                              G_Q=G_quarantine, isolation_time=14,\n",
+    "                              initE=INIT_EXPOSED)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running the model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the max simulation time to 300 days."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T = 300"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Execute the TTI simulation scenario by calling the `run_tti_sim()` function, which runs a custom simulation loop that implements the [TTI Simulation Protocol](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[INTERVENTIONS @ t = 0.64 (4 (0.50%) infected)]\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'sort' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "\u001B[1;32m<ipython-input-28-c1364e940a88>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m run_tti_sim(model, T, \n\u001B[0m\u001B[0;32m      2\u001B[0m             \u001B[0mintervention_start_pct_infected\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mINTERVENTION_START_PCT_INFECTED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage_introductions_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mAVERAGE_INTRODUCTIONS_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m             \u001B[0mtesting_cadence\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_CADENCE\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpct_tested_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mPCT_TESTED_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtest_falseneg_rate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTEST_FALSENEG_RATE\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m             \u001B[0mtesting_compliance_symptomatic\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_SYMPTOMATIC\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_symptomatics\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_SYMPTOMATICS\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m             \u001B[0mtesting_compliance_traced\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_TRACED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_traces\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_TRACES\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;32m~\\PycharmProjects\\seirsplus\\seirsplus\\sim_loops.py\u001B[0m in \u001B[0;36mrun_tti_sim\u001B[1;34m(model, T, intervention_start_pct_infected, average_introductions_per_day, testing_cadence, pct_tested_per_day, test_falseneg_rate, testing_compliance_symptomatic, max_pct_tests_for_symptomatics, testing_compliance_traced, max_pct_tests_for_traces, testing_compliance_random, random_testing_degree_bias, tracing_compliance, num_contacts_to_trace, pct_contacts_to_trace, tracing_lag, isolation_compliance_symptomatic_individual, isolation_compliance_symptomatic_groupmate, isolation_compliance_positive_individual, isolation_compliance_positive_groupmate, isolation_compliance_positive_contact, isolation_compliance_positive_contactgroupmate, isolation_lag_symptomatic, isolation_lag_positive, isolation_lag_contact, isolation_groups, cadence_testing_days, cadence_cycle_length, temporal_falseneg_rates, test_priority)\u001B[0m\n\u001B[0;32m    268\u001B[0m                             \u001B[1;32mif\u001B[0m \u001B[1;34m'last_tested'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtest_priority\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    269\u001B[0m                                 \u001B[1;31m# sort the pool according to the time they were last tested, breaking ties randomly\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 270\u001B[1;33m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mkey\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtestedTime\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcmp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0my\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m*\u001B[0m \u001B[1;36m2\u001B[0m \u001B[1;33m-\u001B[0m \u001B[1;36m1\u001B[0m  \u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0mnumRandomTests\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    271\u001B[0m                             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    272\u001B[0m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtestingPool\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnumpy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mchoice\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnumRandomTests\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mp\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mtestingPool_degreeWeights\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreplace\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
+      "\u001B[1;31mNameError\u001B[0m: name 'sort' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "run_tti_sim(model, T, \n",
+    "            intervention_start_pct_infected=INTERVENTION_START_PCT_INFECTED, average_introductions_per_day=AVERAGE_INTRODUCTIONS_PER_DAY,\n",
+    "            testing_cadence=TESTING_CADENCE, pct_tested_per_day=PCT_TESTED_PER_DAY, test_falseneg_rate=TEST_FALSENEG_RATE, \n",
+    "            testing_compliance_symptomatic=TESTING_COMPLIANCE_SYMPTOMATIC, max_pct_tests_for_symptomatics=MAX_PCT_TESTS_FOR_SYMPTOMATICS,\n",
+    "            testing_compliance_traced=TESTING_COMPLIANCE_TRACED, max_pct_tests_for_traces=MAX_PCT_TESTS_FOR_TRACES,\n",
+    "            testing_compliance_random=TESTING_COMPLIANCE_RANDOM, random_testing_degree_bias=RANDOM_TESTING_DEGREE_BIAS,\n",
+    "            tracing_compliance=TRACING_COMPLIANCE, pct_contacts_to_trace=PCT_CONTACTS_TO_TRACE, tracing_lag=TRACING_LAG,\n",
+    "            isolation_compliance_symptomatic_individual=ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL, isolation_compliance_symptomatic_groupmate=ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE, \n",
+    "            isolation_compliance_positive_individual=ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL, isolation_compliance_positive_groupmate=ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE,\n",
+    "            isolation_compliance_positive_contact=ISOLATION_COMPLIANCE_POSITIVE_CONTACT, isolation_compliance_positive_contactgroupmate=ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE,\n",
+    "            isolation_lag_symptomatic=ISOLATION_LAG_SYMPTOMATIC, isolation_lag_positive=ISOLATION_LAG_POSITIVE, \n",
+    "            isolation_groups=list(teams.values()),\n",
+    "            test_priority = 'last_tested')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total percent infected: 0.88%\n",
+      "total percent fatality: 0.00%\n",
+      "peak  pct hospitalized: 0.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_summary(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualizing the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<Figure size 864x576 with 1 Axes>",
+      "image/png": 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = model.figure_infections(combine_Q_infected=False, plot_Q_R='stacked', plot_Q_S='stacked')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file

From 9800f6f3001f72d60471b2ce3faa9f75c1752921 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Tue, 18 Aug 2020 13:19:25 -0400
Subject: [PATCH 06/19] Remove testing notebook from git tracking

---
 ...EIRS_Workplace_TTI_Demo_timePriority.ipynb | 853 ------------------
 1 file changed, 853 deletions(-)
 delete mode 100644 examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb

diff --git a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb b/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
deleted file mode 100644
index 45a2eec..0000000
--- a/examples/Extended_SEIRS_Workplace_TTI_Demo_timePriority.ipynb
+++ /dev/null
@@ -1,853 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extended SEIRS Workplace TTI Demo\n",
-    "\n",
-    "**In this demonstration we will explore the effect of testing, tracing, and isolation interventions on disease transmission in a workplace setting with a realistic contact network.**\n",
-    "\n",
-    "This notebook provides a demonstration of the functionality of the [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description) and the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). This notebook also offers a sandbox for starting to explore TTI scenarios of your own. \n",
-    "For a more thorough walkthrough of the model, simulation loop, and use of this package, refer to the [SEIRS+ Wiki](https://github.com/ryansmcgee/seirsplus/wiki)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Installing and importing the model code\n",
-    "\n",
-    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
-    "\n",
-    "In this demo we will also use features from the `networks`, `sim_loops`, and `utilities` modules.\n",
-    "\n",
-    "#### Install the package using ```pip```\n",
-    "The package can be installed on your machine by entering this in the command line:\n",
-    "\n",
-    "```pip install seirsplus```\n",
-    "\n",
-    "Then, the ```models```, `networks`, `sim_loops`, and `utilities` modules can be imported as shown here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from seirsplus.models import *\n",
-    "from seirsplus.networks import *\n",
-    "from seirsplus.sim_loops import *\n",
-    "from seirsplus.utilities import *\n",
-    "import networkx\n",
-    "import matplotlib.pyplot as pyplot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### *Alternatively, manually copy the code to your machine*\n",
-    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. For some of the features used in this demo you will also need the `networks`, `sim_loops`, and `utilities` modules. In this case, the easiest way to use the modules is to place your scripts in the same directory as the modules, and import the modules as shown here:*\n",
-    "```python\n",
-    "from models import *\n",
-    "from networks import *\n",
-    "from sim_loops import *\n",
-    "from utilities import *\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set basic parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the workplace size and structure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NUM_COHORTS              = 4\n",
-    "NUM_NODES_PER_COHORT     = 200\n",
-    "NUM_TEAMS_PER_COHORT     = 10\n",
-    "\n",
-    "MEAN_INTRACOHORT_DEGREE  = 6\n",
-    "PCT_CONTACTS_INTERCOHORT = 0.1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "N = NUM_NODES_PER_COHORT*NUM_COHORTS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we set the initial prevalence to be a single case"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "INIT_EXPOSED = 4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------\n",
-    "\n",
-    "## Specifying contact networks\n",
-    "\n",
-    "This package implements models epidemic dynamics for populations with a structured [contact network](Extended-SEIRS-Model-Description#contact-networks). Individuals are represented as nodes in a network, and parameters, contacts, and interventions can be specified on a targeted individual basis. A graph specifying the contact network must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
-    "\n",
-    "This model also supports scenarios where individuals enter quarantine states in which their parameters and interactions may be different from baseline, and a separate graph defining the interactions for individuals in quarantine can be specified (i.e., the [quarantine contact network](Extended-SEIRS-Model-Description#quarantine-contacts)).\n",
-    "\n",
-    "### Workplace Contact Network\n",
-    "\n",
-    "Here we use the [**demographic community network generator**](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#workplace-network) defined in the SEIRS+ package. This function generates a contact network that resembles workplaces and other multi-level modular populations.\n",
-    "\n",
-    "[FARZ](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#farz-networks) network layers are generated to represent cohorts of employees (e.g., departments, floors, shifts). FARZ networks have a tunable community structure, so each cohort includes some number of communities, which can be thought to represent teams (i.e., groups of employees that work closely with each other). Employees may belong to more than one team (specified by a FARZ parameter), but employees belong to only one cohort. An employee's intra-team and intra-cohort contacts are defined by the FARZ cohort network they belong to. A specified percentage of each employee's total number of workplace contacts can be with individuals from other cohorts. An employee's inter-cohort contacts are drawn randomly from the pool of individuals outside their own cohort. \n",
-    "\n",
-    "The number of cohorts, number of employees per cohort, number of teams per cohort, number of teams employees belong to, mean intra-cohort degree, percent of within- and between-team connections, and percent of intra- and inter-cohort connections can be controlled with the arguments to the `generate_demographic_contact_network()` function (some of which are passed as [parameters to the FARZ generator](https://github.com/ryansmcgee/seirsplus/wiki/Network-Generation#FARZ-parameters)).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Baseline:\n",
-      "Degree: mean = 11.11, std = 8.41, 95% CI = (1.00, 29.00)\n",
-      "        coeff var = 0.76\n",
-      "Assortativity:    0.28\n",
-      "Clustering coeff: 0.24\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "G_baseline, cohorts, teams = generate_workplace_contact_network(\n",
-    "                                 num_cohorts=NUM_COHORTS, num_nodes_per_cohort=NUM_NODES_PER_COHORT, \n",
-    "                                 num_teams_per_cohort=NUM_TEAMS_PER_COHORT,\n",
-    "                                 mean_intracohort_degree=MEAN_INTRACOHORT_DEGREE, \n",
-    "                                 pct_contacts_intercohort=PCT_CONTACTS_INTERCOHORT,\n",
-    "                                 farz_params={'alpha':5.0, 'gamma':5.0, 'beta':0.5, 'r':1, 'q':0.0, 'phi':10, \n",
-    "                                              'b':0, 'epsilon':1e-6, 'directed': False, 'weighted': False})\n",
-    "\n",
-    "network_info(G_baseline, \"Baseline\", plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we define the quarantine contact network to be an empty network (i.e., no connections). This represents an assumption that an employee that is in a quarantine state makes no contact with anyone from their workplace."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "G_quarantine = networkx.classes.function.create_empty_copy(G_baseline)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Specifying parameters\n",
-    "\n",
-    "**_The parameter values used in this notebook reflect rough estimates of parameter values for the COVID-19 epidemic (as of 9 Aug 2020)._**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set disease progression rate parameters:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Parameter values are assigned to members of the population on an individual basis. Parameter values can be [specified to the `ExtSEIRSNetworkModel`](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#specifying-parameters) by providing a list of values that gives the *N* values to assign to each individual. The population may be either homogeneous or heterogeneous for a given parameter at the user's discretion. \n",
-    "\n",
-    "**Here we generate distributions of values for each parameter, thus specifying a realistically heterogeneous population.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected latent periods (time in Exposed state) and presymptomatic periods (time in Pre-symptomatic infectious state). The `sigma` and `lamda` rates are calculated as the inverse of the expected exposed and pre-symptomatic periods assigned to each individual, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "latent period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
-      "\n",
-      "pre-symptomatic period:  mean = 2.94, std = 1.72, 95% CI = (0.64, 7.12)\n",
-      "\n",
-      "total incubation period:  mean = 5.14, std = 2.03, 95% CI = (2.17, 10.02)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "latentPeriod_mean, latentPeriod_coeffvar = 3.0, 0.6\n",
-    "SIGMA   = 1 / gamma_dist(latentPeriod_mean, latentPeriod_coeffvar, N)\n",
-    "\n",
-    "presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar = 2.2, 0.5\n",
-    "LAMDA   = 1 / gamma_dist(presymptomaticPeriod_mean, presymptomaticPeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/LAMDA, 1/SIGMA, 1/LAMDA+1/SIGMA], [\"latent period\", \"pre-symptomatic period\", \"total incubation period\"], plot=True, colors=['gold', 'darkorange', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected (a)symptomatic periods (time in symptomatic or asymptomatic state). The `gamma` rates are calculated as the inverse of the expected (a)symptomatic periods assigned to each individual. \n",
-    "\n",
-    "The expected total infectious period for each individual is the sum of their expected pre-symptomatic and (a)symptomatic periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pre-symptomatic period:  mean = 2.19, std = 1.01, 95% CI = (0.71, 4.51)\n",
-      "\n",
-      "(a)symptomatic period:  mean = 4.01, std = 1.54, 95% CI = (1.64, 7.36)\n",
-      "\n",
-      "total infectious period:  mean = 6.20, std = 1.87, 95% CI = (3.09, 10.45)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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aZJ9adKQQbTpSkAhUhSMFqR50pCAiIidNRUFERAIqCiIiElBREBGRgIqCSBWQl5fH1q1bo3bLy8urcH979+5l/Pjxx80r0vbVubm5ZGRklFq+c+dObrjhhuNu/9JLL9GqVSu6det23HWPNXnyZHbu3Bk8vuOOO45qhV3VVLVW2cfSyWsiVcD+/fuPahD3bY0YMYLU1NRyny8uCj/96U8rjPNt21efc845R50QVp5nn32W8ePHn3RRyMjI4JxzzgHgr3/96wnHqCxVsVX2sXSkIFINDR8+nC1btpCVlcWwYcNwd4YNG0ZGRgaZmZnMnDkzWK9k++rc3FwuvfRS2rVrR7t27Xj//fcr3E/JI4jJkydz3XXX0aNHD5o3b86vfvUrIHT9gsWLFzNkyBCGDRtGYWEhw4YNo3379vzgBz/gL3/5SxDvySefJDMzkzZt2jB8+HBefvllli1bRr9+/cjKyuLgwYN07dqV4qnqM2bMIDMzk4yMDB588MEgTsmzkl9++WUGDhwIhI5YMjIyaNOmDV26dCn1+5yKrbKPpSMFkWpo9OjRrFmzhpycHABeeeWVoB32nj17aN++PV26dGH06NFHnbF74MAB3nnnHZKTk9m0aRN9+/blRM4VysnJYcWKFdSpU4cLL7yQe++9l4cffph3332XMWPGkJ2dzcSJE2nQoAFLly7l8OHDdOrUiSuuuIL169cza9YsPvroI+rVq8eXX35Jw4YNefrpp4NtS9q5cycPPvggH3/8MWeeeSZXXHEFs2bNonfv3uXmN2rUKN5++23OPfdc9u7dW+Y6S5YsYe3atZx//vn06NGDV199la5duwatsk877TR+97vfMXbsWB5++GHgP62yIfQPH/7TKnv+/Pm0aNGCW2+9lQkTJjBkyBAGDx7Mu+++y/e+970yO6rGko4URITFixfTt29fatasSePGjbnsssuCRnAlFRQUMHjwYDIzM7nxxhtPeOy+e/fuNGjQgOTkZFq3bn1U471ic+fO5YUXXiArK4uOHTuSl5fHpk2bmDdvHoMGDaJevXoANGzYsMJ9LV26lK5duwY9lPr168eiRYsq3KZTp04MHDiQSZMmlXumd3Gr7Jo1awatsj/88MOgVXZWVhZTpkw56neLtFX2okWLWL9+fdAq28y45ZZbKsw52nSkICIVtm0uady4cTRu3JiVK1dSVFREcnLyCe2nZAO7mjVrltl11N35n//5n1LXO5gzZ84JtZCOtBV1yRbhf/7zn/noo4948803ycrKIicnp9R3M6daq+xj6UghkXy1Nfq3gxXPUpFTU1ltqWfOnElhYSG7d+9m0aJFdOjQodR6X331FU2aNKFGjRpMnTq1wr5JJ+vKK69kwoQJwZXcNm7cyDfffMMVV1zBc889x4EDBwCCy2yW1/q6Y8eO/P3vf2fPnj0UFhYyY8YMLrvsMgAaN27MunXrKCoq4rXXXgu22bJlCx07dmTUqFE0atSI7du3l4p7qrXKPlbMjhTM7DzgBeBsoAiY6O5/NLORwGBgd3jVEe7+Vnibh4DbgULg5+7+dqzySzhHDsLycdGP22EE1C1/lopUjpSUFEaMGBHVeBVJTU2lU6dOZGRk8OMf/5gnn3ySDz74gDZt2mBmPPnkk5x99tmkpqaWal99/fXX89JLL9GtW7cyPwF/W3fccQe5ubm0a9cOdyctLY1Zs2bRo0cPcnJyyM7OJikpiauuuoonnniCgQMHMmTIEOrWrcsHH3wQxGnSpAm//e1v6datG+7OVVddFVyTevTo0fTs2ZPzzjuPjIyM4EvhYcOGsWnTJtyd7t2706ZNm1L5nWqtso8Vs4Z4ZtYEaOLuy82sPvAx0Bu4Cdjv7mOOWb81MAPoAJwDzANauHu5H0WqVUO8dkNjVxTUaK/SqSFeYkqEVtnHqjIN8dx9l7svD9/fB6wDzq1gk17A39z9sLt/CmwmVCBERKSSVMp3CmaWDrQFPgovusfMVpnZc2ZWfJmmc4GSA3g7KKOImNmdZrbMzJbt3r372KdFRGKma9euCXWUcDJiXhTMLAV4Bbjf3b8GJgAXAFnALuD3xauWsXmpsS13n+ju2e6enZaWFqOsRWIvka9lIonhZN5jMS0KZlabUEGY5u6vArj75+5e6O5FwCT+M0S0AzivxOZNgZ2InIKSk5PJy8tTYZCYcXfy8vJOeNpwLGcfGfAssM7dx5ZY3sTdd4UfXgsUf60+G5huZmMJfdHcHFgSq/xE4qlp06bs2LEDDYFKLCUnJ9O0adMT2iaWJ691AvoDq80sJ7xsBNDXzLIIDQ3lAncBuPsnZvYisBY4AvysoplHIomsdu3aNGvWLN5piJQSs6Lg7osp+3uCtyrY5nHg8VjlJCIiFdMZzSIiElBREBGRgIqCiIgEVBRERCSgoiAiIgEVBRERCagoiIhIQEVBREQCKgoiIhJQURARkYCKgoiIBFQUREQkoKIgIiIBFQUREQmoKIiISEBFQUREAioKIiISUFEQEZGAioKIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAIxKwpmdp6ZLTCzdWb2iZndF17e0MzeMbNN4Z9nltjmITPbbGYbzOzKWOUmIiJli+WRwhHgAXdvBVwE/MzMWgPDgfnu3hyYH35M+Lk+wPeBHsB4M6sZw/xEROQYMSsK7r7L3ZeH7+8D1gHnAr2AKeHVpgC9w/d7AX9z98Pu/imwGegQq/xERKS0EyoKZlbDzE4/0Z2YWTrQFvgIaOzuuyBUOICzwqudC2wvsdmO8LJjY91pZsvMbNnu3btPNBUREanAcYuCmU03s9PN7DRgLbDBzIZFugMzSwFeAe53968rWrWMZV5qgftEd8929+y0tLRI0xARkQjUimCd1u7+tZn1A94CHgQ+Bp463oZmVptQQZjm7q+GF39uZk3cfZeZNQG+CC/fAZxXYvOmwM4Ifw85SUVHCijc/q+ox62RUpeaZzaIelwRia1IikLt8D/33sDT7l5gZqU+wR/LzAx4Fljn7mNLPDUbGACMDv98vcTy6WY2FjgHaA4sifg3kZNzpIi9f5ga9bBn3N9fRUEkAUVSFP4C5AIrgUVmdj5Q0TBQsU5Af2C1meWEl40gVAxeNLPbgW3AjQDu/omZvUhoiOoI8DN3LzyB30VERL6l4xYFd/8T8KcSi7aaWbcItltM2d8TAHQvZ5vHgcePF1tERGIjki+aG5vZs2b2/8KPWxMa9hERkVNMJFNSJwNvExrnB9gI3B+rhEREJH4iKQqN3P1FoAjA3Y8AGusXETkFRVIUvjGzVMLnDJjZRcBXMc1KRETiIpLZR78gNF30AjP7B5AG3BDTrEREJC4imX203MwuAy4kNJtog7sXxDwzSXgFUT4pTifEicReuUXBzK4r56kWZkaJM5RFSik6dJiv//xiVGPqhDiR2KvoSOHq8M+zgEuAd8OPuwELARUFEZFTTLlFwd0HAZjZG4T6H+0KP24CPFM56Ukx9yKK9h+IQeCi6McUkYQVyRfN6cUFIexzoEWM8pHyOOSv3hT1sEkXRT2kiCSwSIrCQjN7G5hBaFpqH2BBTLMSEZG4iGT20T1mdi3QJbxooru/Ftu0REQkHiI5UgB4n1DnUkftrEVETlmRNMS7iVAhuAG4CfjIzHTymojIKSiSI4VfA+3d/QsAM0sD5gEvxzIxERGpfJH0PqpRXBDC8iLcTkREEkwkRwpzSsw+AriZ0LWaRUTkFBPJ7KNhZnY9octrGpp9JKeYvLw89u/fH/W4KSkppKamRj2uSCxFNPvI3V8BXolxLiIVKsjPZ+fWrVGPm5+fz5gxY6Ied8SIESoKknCOWxTCjfF+R6gHkoVv7u6nxzg3kaMcKSzkiSdHRz3u0KFDox5TJFFFcqTwJHC1u6+LdTIiIhJfkcwi+lwFQUSkeojkSGGZmc0EZgGHixfqegoiIqeeSIrC6cAB4IoSyxxdT0FE5JQTyZTUQZWRiIiIxF/Mzkw2s+fM7AszW1Ni2Ugz+8zMcsK3q0o895CZbTazDWZ2ZazyEhGR8sWyXcVkoEcZy8e5e1b49haAmbUmdJ2G74e3GW9mNWOYm4iIlCFmRcHdFwFfRrh6L+Bv7n7Y3T8FNgMdYpWbiIiULZKT184AbgXSS67v7j8/yX3eY2a3AsuAB9z938C5wIcl1tkRXlZWPncCdwJ85zvfOckURESkLJEcKbxFqCCsBj4ucTsZE4ALgCxgF/D78HIrY10vK4C7T3T3bHfPTktLO8k0RESkLJFMSU12919EY2fu/nnxfTObBLwRfrgDOK/Eqk2BndHYp4iIRC6SI4WpZjbYzJqYWcPi28nszMyalHh4LVA8M2k20MfM6phZM6A5uuyniEili+RIIR94itAV2IqHdBz4bkUbmdkMoCvQyMx2AI8AXc0sK7x9LnAXgLt/YmYvAmsJXQv6Z+5eeKK/jIiIfDuRFIVfAN9z9z0nEtjd+5ax+NkK1n8cePxE9iEiItEVyfDRJ4TaXIiIyCkukiOFQiDHzBZwdEO8k52SKtWAFxWxb9++qMZM8qKoxhOR0iIpCrPCN5GIucOKFSuiGrO7D4hqPBEpLZKGeFMqIxEREYm/SM5o/pQyTiRz9wpnH4mISOKJZPgou8T9ZOBG4KTOUxARkartuLOP3D2vxO0zd/8DcHkl5CYiIpUskuGjdiUe1iB05FA/ZhmJiEjcRDJ89PsS948QOhP5pphkIyIicRXJ7KNulZGIiIjEXyTDR3WA6yl9PYVRsUtLRETiIZLho9eBrwhdQ+HwcdYVEZEEFklRaOruZV1rWURETjGRNMR738wyY56JiIjEXSRHCp2BgeEzmw8TunSmu/sPYpqZiIhUukiKwo9jnoWIiFQJkUxJ3VoZiYiISPxFcqQgpzBLqs3pN7ePetyaDSzqMRPR1q3R/UyVkpJCampqVGOKlKSiUN0VHqJozvCoh611x/NRj5loDh48yLhx46Iac8SIESoKElORzD4SEZFqQkVBREQCKgoiIhJQURARkYCKgoiIBGI2+8jMngN6Al+4e0Z4WUNgJqGOq7nATe7+7/BzDwG3A4XAz9397VjlJokpKSmJu6+N/qU8GiXVjXpMkUQVyympk4GngRdKLBsOzHf30WY2PPz4QTNrDfQBvg+cA8wzsxbuXhjD/CTBWH4BW37zh6jHTZ8a/ZgiiSpmw0fuvgj48pjFvYAp4ftTgN4llv/N3Q+7+6fAZqBDrHITEZGyVfZ3Co3dfRdA+OdZ4eXnAttLrLcjvExERCpRVfmiuayeCF7mimZ3mtkyM1u2e/fuGKclIlK9VHZR+NzMmgCEf34RXr4DOK/Eek2BnWUFcPeJ7p7t7tlpaWkxTVZEpLqp7KIwGxgQvj+A0KU+i5f3MbM6ZtYMaA4sqeTcRESqvVhOSZ0BdAUamdkO4BFgNPCimd0ObANuBHD3T8zsRWAtcAT4mWYeiYhUvpgVBXfvW85T3ctZ/3Hg8VjlIyIix1dVvmgWEZEqQEVBREQCKgoiIhJQURARkYCKgoiIBFQUREQkoKIgIiIBFQUREQmoKIiISEBFQUREAioKIiISiOXlOKu+g3mQvz+6MYvyoxtPRKQSVe+ikL8fljwR3ZjthkY3nohIJdLwkYiIBFQUREQkUL2Hj0QS0NatW6MeMyUlhdTU1KjHlcSjoiCSQA4ePMi4ceOiHnfEiBEqCgKoKAhQUFAQ9ZhJUY8oIpVBRUHYs2dP1GOeFvWIIlIZ9EWziIgEVBRERCSgoiAiIgEVBRERCagoiIhIQEVBREQCcZmSama5wD6gEDji7tlm1hCYCaQDucBN7v7veOQnIlJdxfNIoZu7Z7l7dvjxcGC+uzcH5ocfi4hIJapKw0e9gCnh+1OA3nHMRUSkWorXGc0OzDUzB/7i7hOBxu6+C8Ddd5nZWWVtaGZ3AncCfOc736msfCPmXkTR/gNRj2t41GOKiBwrXkWhk7vvDP/jf8fM1ke6YbiATATIzs6uev8pHfJXb4p62Dqdoh5SRKSUuBQFd98Z/vmFmb0GdAA+N7Mm4aOEJsAX8chNqp+kpCTuvvamqMdtlFQ36jFFYq3Si4KZnQbUcPd94ftXAKOA2cAAYIPiNpwAAAZwSURBVHT45+uVnZtUT5ZfwJbf/CHqcdOnRj+mSKzF40ihMfCamRXvf7q7zzGzpcCLZnY7sA24MQ65iYhUa5VeFNz9n0CbMpbnAd0rOx8REfmPqjQlVURE4kxFQUREArrymsSEJSXR6dFBUY1Zu4E+w4jEmoqCxEbRIQ68PjSqIe0Hc6IaT0RK00cvEREJqCiIiEhARUFERAIqCiIiElBREBGRgGYfiQgAW7dujXrMlJQUUlNTox5XYkdFQUQ4ePAg48aNi3rcESNGqCgkGA0fiYhIQEcKIjESi+s06BoNEmsqCiIxEovrNOgaDRJrGj4SEZGAioKIiAQ0fJRACgoKoh6zTtQjihxNU10Ti4pCAtmzZ0/UY6ZEPaLIf2iqa+LR8JGIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAKakioJw5KS6PTooKjHrd0gcT4bxaKfEqinEkBeXh779++PetxEO6eiyhUFM+sB/BGoCfzV3UfHOSWpKooOceD1oVEPaz+YE/WYsRKLfkoALWaOT7hiE+2T4vLz8xkzZkxUY0LinVNRpYqCmdUEngF+BOwAlprZbHdfG9/MRE5tsSo2sWrgF4uT4oYOjf4HjmKJdFZ3lSoKQAdgs7v/E8DM/gb0AmJSFIqOFOD7D0Q1prmrHYWIBBLtrG5z96gHPVlmdgPQw93vCD/uD3R093tKrHMncGf44YXAhm+xy0ZA9HtHxEYi5QqJla9yjZ1EyjeRcoVvl+/57p5W1hNV7UjBylh2VNVy94nAxKjszGyZu2dHI1asJVKukFj5KtfYSaR8EylXiF2+VW3axQ7gvBKPmwI745SLiEi1U9WKwlKguZk1M7MkoA8wO845iYhUG1Vq+Mjdj5jZPcDbhKakPufun8Rwl1EZhqokiZQrJFa+yjV2EinfRMoVYpRvlfqiWURE4quqDR+JiEgcqSiIiEigWhYFM+thZhvMbLOZDY93PhUxs/PMbIGZrTOzT8zsvnjndDxmVtPMVpjZG/HO5XjM7Awze9nM1odf44vjnVN5zGxo+D2wxsxmmFlyvHMqycyeM7MvzGxNiWUNzewdM9sU/nlmPHMsVk6uT4XfB6vM7DUzOyOeOZZUVr4lnvulmbmZNYrGvqpdUSjRSuPHQGugr5m1jm9WFToCPODurYCLgJ9V8XwB7gPWxTuJCP0RmOPuLYE2VNG8zexc4OdAtrtnEJqI0Se+WZUyGehxzLLhwHx3bw7MDz+uCiZTOtd3gAx3/wGwEXiospOqwGRK54uZnUeoLdC2aO2o2hUFSrTScPd8oLiVRpXk7rvcfXn4/j5C/7TOjW9W5TOzpsBPgL/GO5fjMbPTgS7AswDunu/ue+ObVYVqAXXNrBZQjyp2Do+7LwK+PGZxL2BK+P4UoHelJlWOsnJ197nufiT88ENC50lVCeW8tgDjgF9xzEm+30Z1LArnAttLPN5BFf4nW5KZpQNtgY/im0mF/kDoTVoU70Qi8F1gN/B8eLjrr2Z2WryTKou7fwaMIfSJcBfwlbvPjW9WEWns7rsg9AEHOCvO+UTqNuD/xTuJipjZNcBn7r4ymnGrY1E4biuNqsjMUoBXgPvd/et451MWM+sJfOHuH8c7lwjVAtoBE9y9LfANVWd44yjhsfheQDPgHOA0M7slvlmdmszs14SGbafFO5fymFk94NfAw9GOXR2LQsK10jCz2oQKwjR3fzXe+VSgE3CNmeUSGpa73Mz+b3xTqtAOYIe7Fx95vUyoSFRFPwQ+dffd7l4AvApcEuecIvG5mTUBCP/8Is75VMjMBgA9gX5etU/iuoDQB4SV4b+3psByMzv72waujkUhoVppmJkRGvNe5+5j451PRdz9IXdv6u7phF7Xd929yn6adfd/AdvN7MLwou7EqE17FGwDLjKzeuH3RHeq6Jfix5gNDAjfHwC8HsdcKhS+wNeDwDXuHt2e+lHm7qvd/Sx3Tw//ve0A2oXf099KtSsK4S+SiltprANejHErjW+rE9Cf0KfunPDtqngndQq5F5hmZquALOCJOOdTpvDRzMvAcmA1ob/dKtWWwcxmAB8AF5rZDjO7HRgN/MjMNhGaJVMlrqRYTq5PA/WBd8J/Z3+Oa5IllJNvbPZVtY+QRESkMlW7IwURESmfioKIiARUFEREJKCiICIiARUFEREJqCiIiEhARUFERAL/H/Fx2N8iHDi3AAAAAElFTkSuQmCC\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "symptomaticPeriod_mean, symptomaticPeriod_coeffvar = 4.0, 0.4\n",
-    "GAMMA   = 1 / gamma_dist(symptomaticPeriod_mean, symptomaticPeriod_coeffvar, N)\n",
-    "\n",
-    "infectiousPeriod = 1/LAMDA + 1/GAMMA\n",
-    "\n",
-    "dist_info([1/LAMDA, 1/GAMMA, 1/LAMDA+1/GAMMA], [\"pre-symptomatic period\", \"(a)symptomatic period\", \"total infectious period\"], plot=True, colors=['darkorange', 'crimson', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of expected onset-to-hospitalization periods (time in symptomatic state before entering hospitalized state for those with severe cases) and hospitalization-to-discharge periods (time in hospitalized state for those with non-fatal cases). The `eta` and `gamma_H` rates are calculated as the inverse of the expected onset-to-hospitalization periods and hospitalization-to-discharge periods assigned to each individual, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
-      "\n",
-      "hospitalization-to-discharge period:  mean = 11.38, std = 5.29, 95% CI = (3.49, 23.54)\n",
-      "\n",
-      "onset-to-discharge period:  mean = 22.51, std = 7.16, 95% CI = (10.62, 39.43)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar = 11.0, 0.45\n",
-    "ETA     = 1 / gamma_dist(onsetToHospitalizationPeriod_mean, onsetToHospitalizationPeriod_coeffvar, N)\n",
-    "\n",
-    "hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar = 11.0, 0.45\n",
-    "GAMMA_H = 1 / gamma_dist(hospitalizationToDischargePeriod_mean, hospitalizationToDischargePeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/ETA, 1/GAMMA_H, 1/ETA+1/GAMMA_H], [\"onset-to-hospitalization period\", \"hospitalization-to-discharge period\", \"onset-to-discharge period\"], plot=True, colors=['crimson', 'violet', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generate a distribution of hospitalization-to-death periods (time in hospitalized state for those with fatal cases). The `mu_H` rates are calculated as the inverse of the expected hospitalization-to-death periods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "onset-to-hospitalization period:  mean = 11.12, std = 5.10, 95% CI = (3.72, 22.73)\n",
-      "\n",
-      "hospitalization-to-death period:  mean = 6.87, std = 3.10, 95% CI = (2.01, 13.61)\n",
-      "\n",
-      "onset-to-death period:  mean = 17.99, std = 6.08, 95% CI = (8.34, 31.59)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar = 7.0, 0.45\n",
-    "MU_H    = 1 / gamma_dist(hospitalizationToDeathPeriod_mean, hospitalizationToDeathPeriod_coeffvar, N)\n",
-    "\n",
-    "dist_info([1/ETA, 1/MU_H, 1/ETA+1/MU_H], [\"onset-to-hospitalization period\", \"hospitalization-to-death period\", \"onset-to-death period\"], plot=True, colors=['crimson', 'darkgray', 'black'], reverse_plot=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set severity parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Specify the percentage of cases that are asymptomatic. This percentage of case will progress from the pre-symptomatic state to the asymptomatic state, rather than to the symptomatic state."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_ASYMPTOMATIC = 0.25"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the case hospitalization rate. The value used here is approximately the age-frequency-weighted average of age-stratified hospitalization rates for working age adults using data from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_HOSPITALIZED = 0.035"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the case fatality rate for hospitalized cases. The value used here is approximately the age-frequency-weighted average of age stratified hospitalization fatality rates for working age adults, again using figures from [Verity et al. (2020)](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "PCT_FATALITY = 0.08"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set transmission parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [Extended SEIRS Network Model](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#stochastic-network-model-implementation) model considers two modes of disease transmission: a well-mixed mode of [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) and a contact network based mode of [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The propensity for a given individual to become exposed due to global transmission depends on the mean transmissibility of all infectious individuals in the population; the propensity for a given individual to become exposed due to local transmission depends on the pairwise transmissibilities between the focal node and its infectious contacts in the network (see [Transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#transmission) and [Model Equations](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#model-equations) for more information about these calculations). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The transmissibility parameter *β* can be related to the basic reproduction number *R<sub>0</sub>* (i.e., the expected number of new infections generated by a single infectious individual in a completely susceptible population) by the standard formula: *β = R<sub>0</sub>𝛾*. *R<sub>0</sub>* is a more interpretable parameter, so we specify transmissibility in terms of *R<sub>0</sub>* and then calculate the corresponding *β* values.\n",
-    "\n",
-    "First, we generate a distribution of individual *R<sub>0</sub>* values (i.e., the expected number of new infections generated by a single *particular* infectious individual in a completely susceptible population). Of course, this means that transmissibility is heterogeneous in this population. The coefficient of variation is an important parameter for the individual *R<sub>0</sub>* distribution in that it tunes the degree of superspreading in the heterogeneous transmissibility. The distribution used in this example has a relatively low coefficient of variation, so most individuals have around the same degree of transmissibility. But a higher coefficient of variation (e.g., 2.0) would give a long right tail in idividual transmissibility representing a small number of individuals contributing many cases while the majority cases contribute less than 1 on average when they are infectious."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Individual R0:  mean = 1.98, std = 0.41, 95% CI = (1.32, 2.89)\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "R0_mean     = 2.0\n",
-    "R0_coeffvar = 0.2\n",
-    "\n",
-    "R0 = gamma_dist(R0_mean, R0_coeffvar, N)\n",
-    "\n",
-    "dist_info(R0, \"Individual R0\", bin_size=0.1, plot=True, colors='crimson')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Individuals are ultimately assigned an [*Individual Transmissibility Value*](https://github.com/ryansmcgee/seirsplus/wiki/ExtSEIRSNetworkModel-Class#transmissibility-parameters) (*β<sup>i</sup>*), which are stored in the `beta` attribute of the model object. \n",
-    "\n",
-    "The means of the Individual Transmissibility Values for infectious subpopulations are used to calculate the [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission) terms. Individual Transmissibility Values may also be used to generate the Pairwise Transmissibility Values used for [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission) terms, as we will specify in a few steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BETA = 1/infectiousPeriod * R0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the stochastic network model, an individual comes into contact with a random individual from the population at large (e.g., in a public space) with probability *p* or with an individual from their set of close contacts with probability *(1-p)*. Transmission that occurs between an individual and the population at large is referred to as [global transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#global-transmission), and transmission between an individual and one of their close contacts (network neighbors) is referred to as [local transmission](https://github.com/ryansmcgee/seirsplus/wiki/Extended-SEIRS-Model-Description#local-transmission). The parameter *p* defines the locality of the network: for *p=0* an individual only interacts with their close contacts, while *p=1* represents a uniformly mixed population.\n",
-    "\n",
-    "Here we set *p* to reflect 40% of interactions being with incidental or casual contacts outside their set of close contacts."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P_GLOBALINTXN = 0.4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Testing, Tracing, & Isolation (TTI) intervention protocol parameters:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we specify the parameters that govern the Testing, Tracing, and Isolation protocol that is implemented by the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop). The implementation of this TTI protocol and the interpretation of these parameters is desribed in detail on the [TTI Simulation Loop wiki page](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) (but these parameters are briefly explained as code comments below).\n",
-    "\n",
-    "**The scenario set up in the steps that follow involves the entire workforce being tested on a weekly basis, a 2-day test turn around time, 50% of symptomatic individuals self-reporting and getting tested within 1 day of onset, 30% of symptomatics self-isolating even without a positive test, and teams of detected positive cases being proactively isolated. A new exogenous exposures comes into the workplace about once a week.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "INTERVENTION_START_PCT_INFECTED = 0/100\n",
-    "AVERAGE_INTRODUCTIONS_PER_DAY   = 1/14          # expected number of new exogenous exposures per day\n",
-    "\n",
-    "TESTING_CADENCE                 = 'weekly'      # how often to do testing (other than self-reporting symptomatics who can get tested any day)\n",
-    "PCT_TESTED_PER_DAY              = 1.0           # max daily test allotment defined as a percent of population size\n",
-    "TEST_FALSENEG_RATE              = 'temporal'    # test false negative rate, will use FN rate that varies with disease time\n",
-    "MAX_PCT_TESTS_FOR_SYMPTOMATICS  = 1.0           # max percent of daily test allotment to use on self-reporting symptomatics\n",
-    "MAX_PCT_TESTS_FOR_TRACES        = 0.0           # max percent of daily test allotment to use on contact traces\n",
-    "RANDOM_TESTING_DEGREE_BIAS      = 0             # magnitude of degree bias in random selections for testing, none here\n",
-    "\n",
-    "PCT_CONTACTS_TO_TRACE           = 0.0           # percentage of primary cases' contacts that are traced\n",
-    "TRACING_LAG                     = 2             # number of cadence testing days between primary tests and tracing tests\n",
-    "\n",
-    "ISOLATION_LAG_SYMPTOMATIC       = 1             # number of days between onset of symptoms and self-isolation of symptomatics\n",
-    "ISOLATION_LAG_POSITIVE          = 2             # test turn-around time (TAT): number of days between administration of test and isolation of positive cases\n",
-    "ISOLATION_LAG_CONTACT           = 0             # number of days between a contact being traced and that contact self-isolating\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set intervention compliance parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, we specify the compliance rates (i.e., the percentage of individuals who are compliant) for each intervention type. See the [TTI Simulation Loop](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop) documentation for more information about compliance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TESTING_COMPLIANCE_RATE_SYMPTOMATIC                  = 0.5    \n",
-    "TESTING_COMPLIANCE_RATE_TRACED                       = 0.0\n",
-    "TESTING_COMPLIANCE_RATE_RANDOM                       = 1.0  # Assume employee testing is mandatory, so 100% compliance\n",
-    "\n",
-    "TRACING_COMPLIANCE_RATE                              = 0.0\n",
-    "\n",
-    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL     = 0.3\n",
-    "ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE      = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL        = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE         = 0.8  # Isolate teams with a positive member, but suppose 20% of employees are essential workforce\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT           = 0.0\n",
-    "ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE  = 0.0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we randomly assign a `True/False` compliance to each individual according to the rates set above. Individuals whose compliance is set to `True` for a given intervention will participate in that intervention, individuals set to `False` will not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "TESTING_COMPLIANCE_RANDOM                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_RANDOM)\n",
-    "TESTING_COMPLIANCE_TRACED                        = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_TRACED)\n",
-    "TESTING_COMPLIANCE_SYMPTOMATIC                   = (numpy.random.rand(N) < TESTING_COMPLIANCE_RATE_SYMPTOMATIC)\n",
-    "\n",
-    "TRACING_COMPLIANCE                               = (numpy.random.rand(N) < TRACING_COMPLIANCE_RATE)\n",
-    "\n",
-    "ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL      = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_INDIVIDUAL)\n",
-    "ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE       = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_SYMPTOMATIC_GROUPMATE)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL         = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_INDIVIDUAL)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE          = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_GROUPMATE)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_CONTACT            = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACT)\n",
-    "ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE   = (numpy.random.rand(N) < ISOLATION_COMPLIANCE_RATE_POSITIVE_CONTACTGROUPMATE)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing the model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\boaz\\PycharmProjects\\seirsplus\\seirsplus\\models.py:2143: RuntimeWarning: invalid value encountered in true_divide\n",
-      "  self.delta_Q             = numpy.log(self.degree_Q)/numpy.log(numpy.mean(self.degree_Q)) if self.parameters['delta_Q'] is None else numpy.array(self.parameters['delta_Q']) if isinstance(self.parameters['delta_Q'], (list, numpy.ndarray)) else numpy.full(fill_value=self.parameters['delta_Q'], shape=(self.numNodes,1))\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = ExtSEIRSNetworkModel(G=G_baseline, p=P_GLOBALINTXN,\n",
-    "                              beta=BETA, sigma=SIGMA, lamda=LAMDA, gamma=GAMMA, \n",
-    "                              gamma_asym=GAMMA, eta=ETA, gamma_H=GAMMA_H, mu_H=MU_H, \n",
-    "                              a=PCT_ASYMPTOMATIC, h=PCT_HOSPITALIZED, f=PCT_FATALITY,              \n",
-    "                              G_Q=G_quarantine, isolation_time=14,\n",
-    "                              initE=INIT_EXPOSED)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running the model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Set the max simulation time to 300 days."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "T = 300"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Execute the TTI simulation scenario by calling the `run_tti_sim()` function, which runs a custom simulation loop that implements the [TTI Simulation Protocol](https://github.com/ryansmcgee/seirsplus/wiki/TTI-Simulation-Loop)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[INTERVENTIONS @ t = 0.64 (4 (0.50%) infected)]\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'sort' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
-      "\u001B[1;32m<ipython-input-28-c1364e940a88>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m run_tti_sim(model, T, \n\u001B[0m\u001B[0;32m      2\u001B[0m             \u001B[0mintervention_start_pct_infected\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mINTERVENTION_START_PCT_INFECTED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0maverage_introductions_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mAVERAGE_INTRODUCTIONS_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m             \u001B[0mtesting_cadence\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_CADENCE\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mpct_tested_per_day\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mPCT_TESTED_PER_DAY\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtest_falseneg_rate\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTEST_FALSENEG_RATE\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      4\u001B[0m             \u001B[0mtesting_compliance_symptomatic\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_SYMPTOMATIC\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_symptomatics\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_SYMPTOMATICS\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      5\u001B[0m             \u001B[0mtesting_compliance_traced\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mTESTING_COMPLIANCE_TRACED\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mmax_pct_tests_for_traces\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mMAX_PCT_TESTS_FOR_TRACES\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;32m~\\PycharmProjects\\seirsplus\\seirsplus\\sim_loops.py\u001B[0m in \u001B[0;36mrun_tti_sim\u001B[1;34m(model, T, intervention_start_pct_infected, average_introductions_per_day, testing_cadence, pct_tested_per_day, test_falseneg_rate, testing_compliance_symptomatic, max_pct_tests_for_symptomatics, testing_compliance_traced, max_pct_tests_for_traces, testing_compliance_random, random_testing_degree_bias, tracing_compliance, num_contacts_to_trace, pct_contacts_to_trace, tracing_lag, isolation_compliance_symptomatic_individual, isolation_compliance_symptomatic_groupmate, isolation_compliance_positive_individual, isolation_compliance_positive_groupmate, isolation_compliance_positive_contact, isolation_compliance_positive_contactgroupmate, isolation_lag_symptomatic, isolation_lag_positive, isolation_lag_contact, isolation_groups, cadence_testing_days, cadence_cycle_length, temporal_falseneg_rates, test_priority)\u001B[0m\n\u001B[0;32m    268\u001B[0m                             \u001B[1;32mif\u001B[0m \u001B[1;34m'last_tested'\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mtest_priority\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    269\u001B[0m                                 \u001B[1;31m# sort the pool according to the time they were last tested, breaking ties randomly\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 270\u001B[1;33m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0msort\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0mkey\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mi\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mmodel\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtestedTime\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mi\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mcmp\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;32mlambda\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m,\u001B[0m\u001B[0my\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0mx\u001B[0m\u001B[1;33m-\u001B[0m\u001B[0my\u001B[0m \u001B[1;32melse\u001B[0m \u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m0\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m1\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m*\u001B[0m \u001B[1;36m2\u001B[0m \u001B[1;33m-\u001B[0m \u001B[1;36m1\u001B[0m  \u001B[1;33m)\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;33m:\u001B[0m\u001B[0mnumRandomTests\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    271\u001B[0m                             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    272\u001B[0m                                 \u001B[0mrandomSelection\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtestingPool\u001B[0m\u001B[1;33m[\u001B[0m\u001B[0mnumpy\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandom\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mchoice\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtestingPool\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnumRandomTests\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mp\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mtestingPool_degreeWeights\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mreplace\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;32mFalse\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
-      "\u001B[1;31mNameError\u001B[0m: name 'sort' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "run_tti_sim(model, T, \n",
-    "            intervention_start_pct_infected=INTERVENTION_START_PCT_INFECTED, average_introductions_per_day=AVERAGE_INTRODUCTIONS_PER_DAY,\n",
-    "            testing_cadence=TESTING_CADENCE, pct_tested_per_day=PCT_TESTED_PER_DAY, test_falseneg_rate=TEST_FALSENEG_RATE, \n",
-    "            testing_compliance_symptomatic=TESTING_COMPLIANCE_SYMPTOMATIC, max_pct_tests_for_symptomatics=MAX_PCT_TESTS_FOR_SYMPTOMATICS,\n",
-    "            testing_compliance_traced=TESTING_COMPLIANCE_TRACED, max_pct_tests_for_traces=MAX_PCT_TESTS_FOR_TRACES,\n",
-    "            testing_compliance_random=TESTING_COMPLIANCE_RANDOM, random_testing_degree_bias=RANDOM_TESTING_DEGREE_BIAS,\n",
-    "            tracing_compliance=TRACING_COMPLIANCE, pct_contacts_to_trace=PCT_CONTACTS_TO_TRACE, tracing_lag=TRACING_LAG,\n",
-    "            isolation_compliance_symptomatic_individual=ISOLATION_COMPLIANCE_SYMPTOMATIC_INDIVIDUAL, isolation_compliance_symptomatic_groupmate=ISOLATION_COMPLIANCE_SYMPTOMATIC_GROUPMATE, \n",
-    "            isolation_compliance_positive_individual=ISOLATION_COMPLIANCE_POSITIVE_INDIVIDUAL, isolation_compliance_positive_groupmate=ISOLATION_COMPLIANCE_POSITIVE_GROUPMATE,\n",
-    "            isolation_compliance_positive_contact=ISOLATION_COMPLIANCE_POSITIVE_CONTACT, isolation_compliance_positive_contactgroupmate=ISOLATION_COMPLIANCE_POSITIVE_CONTACTGROUPMATE,\n",
-    "            isolation_lag_symptomatic=ISOLATION_LAG_SYMPTOMATIC, isolation_lag_positive=ISOLATION_LAG_POSITIVE, \n",
-    "            isolation_groups=list(teams.values()),\n",
-    "            test_priority = 'last_tested')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "total percent infected: 0.88%\n",
-      "total percent fatality: 0.00%\n",
-      "peak  pct hospitalized: 0.00%\n"
-     ]
-    }
-   ],
-   "source": [
-    "results_summary(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-------"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualizing the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 864x576 with 1 Axes>",
-      "image/png": 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vXs0DDzxAc3MzO3bs4MCBA7S1tfHUU09x6623UlVVNaKfr1zPOP89cCcc377vApryv7qBW4FPlOm6JUkj4I6zPR762I6zTziXR7l3nFetWsXmzZtZvXo1q1at4qabbmLhwoVMmzaNnp4epkyZQltbG+vWraO2tpa9e/eyZs0apk6dymWXXUZLS0tRP1/Jn3EOIfwhsD/G+N0Qwm35T98O3AFsAs4G1gPXhBAuAO6LMT5yksu6HrgeYN68eaW+qZKkvEh0x9keF/2Jn/q8XTk1vOE8Gt5w3is+dzqdePkNDXzyk58km81y3XXXMXXqVACefPJJGhsbOXr0KG95y1v49Kc/zX333cdnP/tZGhoaGBoaoq6ujuHh4RE/2wwQYiztP7lCCD8m9++4CFwAPAe8I8a4J4RQBdwPXAfcA1wNfD3GeMWpLnflypVx48aNJb2tkqScP/7eH3Og9wDnTTuvooYc2y51P7r7UXZ27eSx9zxGbVVt2nc9lVhvby933303l156KUuWLBnpxZz01YYlf8Y5xnjR8WsN4YfAn8QY9+Q/dT1wb/7jDLnhuqnUt0GSdPoO93keZ3vs98S6ie44j2ENDQ3ceOONZbv8M3Y6uhDCBOCSGOM3YoyHgT3Aw8DdZ+o2SJJOrn+4P/WhxrbL3Z7HWcUo6zsHxhgvOeHjDmDVCX1DOa9bklS4SKSuqi71oca2y93/7af/Le27m0Yx3wBFkgTApPpJqQ81tl3uPnYeZ2kkHJwlSYA7zvb46Na63JtnlPrkCBofHJwlSYA7zvb46NdOeW3adzWNYg7OkiQAd5ztcdGbD24GfHGgRsbBWZIEESbXT059qLHtcrc7ziqGg7MkCYBDfYdSH2psu9w9sW4i4I7zaLZmzRo6OztTue6yno5OkjR6uONsj4euydTw9MGn0767jV2f+n/g4N6R//4pM+DP/ybxWzo7O2lpaRn5dRTBwVmSBLjjbI+P/ruNf5f2XW1sO7gXWicX9/sTdHV10dSU3ptOu6ohSSISmdIwJfWhxrbL3Uf6j6R9d1MRtm7dyqJFi1K7fgdnSRIAB3sPpj7U2Ha5+/iOs2fVGJW2bNmS6uDsqoYkCYCB4YHUhxrbLnfXZmp5+oA7zqPVli1buOiii/jhD3/IQw89xMDAAGeffTaHDh3irW99KzU1NXz3u9/l0KFD/OVf/iXNzc0lvX6fcZYkAe442+Ojj5/H2bNqjEpbt25l8eLFPP300/z5n/85l1xyCZ2dnVx33XUsX76c+++/nzlz5jB79uyynHnDZ5wlSQBMbvA8zvbYb3ecy2zKjOLPqpHgzjvvBGDfvn186UtfYmBggGw2e/wsG3PnzqWzs5NFixZx1llnjfx2nISDsySJSHTH2R4XPbFuIts7t6d9lxu7TnEquVKZPn06a9asecXn/8t/+S9lvV5XNSRJgDvO9vjoZVOXAb44cLS78cYbU7leB2dJEgB11e4422O/fWGgiuHgLEkCYEq953G2x34f23H2GWeNhIOzJIkYIwf73HG2x35PqpuU9t1No5iDsyQJgP6h/tSHGtsudy+fuhzwdHQaGQdnSRLgjrM9PvqpA0+lfVfTKObgLEkCYGr91NSHGtsud3seZxXDwVmSRCRyoO9A6kONbZe7J9ZPTPvuplHMwVmSBLjjbI+PXj7FHWeNnO8cKEkC3HG2x0d/8rFPpn1XU5HWrFnDwMAAADU1Ndx9992EEM7IdTs4S5IAmNrgjrM99vtw32HA8ziXzQ8ug+5tI//9TfPg17+b+C1Hjx5l7dq1I7+OIjg4S5IAONDrjrM99ntS/SS2dRYx2ClZ9zaon1nc70/Q1dVFfX39yC+/SO44S5KIMdI/7I6zPfb7+HmcfcZ5VNq6dSsvvfQS1157Lddeey0//OEPz+j1+4yzJAmA+qr61Ica2y53u+M8um3ZsoXrrruOP/zDP0zl+n3GWZIEuONsj48+tuOs0WnLli0sWbIktev3GWdJEuCOsz0++tiOs6ejG522bt3Khg0b+PznPw/AF77whTO68+zgLEkiEukb7kt9qLHtcndzTTM/2/+ztO9yY1fTvOLPqpHgzjvvHPlll4CDsyQJcMfZHh/9qcc+BfjiwLI5xankRjt3nCVJAExrmJb6UGPb5e7D/e44a+QcnCVJAOzv3Z/6UGPb5e5JdZPSvqtpFHNwliQBuONsj4s+f9r5ad/VNIo5OEuSiDG642yPi/7ZgZ8d/zsvnS4HZ0kSANMa3XG2x357HmcVw8FZkgTA/h53nO2x35PrJwOeVUMj4+AsSQKgf7g/9aHGtsvd7jirGA7OkiQikbqqutSHGtsudx978xN3nDUSDs6SJMAdZ3t8tDvOKoaDsyQJgAM9B1Ifamy73D2pPnceZ3ecNRJlGZxDCFUhhHtCCA+HEH4cQlgcQrg8hLAhhLA2hJDJf99nQggLynEbJEmnx/M42+OhL5h2Qdp3NY1i5XrG+bcBYoxvBj4IfBJ4L3ApsBM4P4SwHOiIMb5UptsgSToNnsfZHg/9xP4n0r6raRSrLseFxhgfCCF8M5/zgb1AM9CU/9UNfIjcMC1JSlkkuuNsj4t2x1nFKMvgDBBjHAoh3Ae8C7gK2A7cAWwCzgbWA9eEEC4A7osxPlKu2yJJOrX9vftp7GysqCHHtkvdk+sn81LHS2nf3TRKlfXFgTHGPwCWAF8AtsUY3w38LbAG+CJwGXAj8IFX+/0hhOtDCBtDCBv3799fzpsqSeNe35A7zvbY7wunXwh4OjqNTLleHHhtCOG2fPYAWWA439cD955w/ZHc+sYrxBjvijGujDGunDZtWjluqiQpr77aHWd77Pfj+x5P+66mUaxcqxpfBf45hPBjoAa4OcbYF0KYAFwSY1wFEELYAzwMfK5Mt0OSVIAYI9Ma3HG2x34f6juU+zvv6eg0AuV6cWA38Luv8vkOYNUJfUM5rl+SdPrccbbHQ7vjrGL4BiiSJMAdZ3t8dNv0NsBnnDUyDs6SJMAdZ3t89OP73XHWyDk4S5IAmN4wPfWhxrbL3cd3nD2rhkbAwVmSRCSyr3df6kONbZe7J9dPTvvuplHMwVmSBLjjbI+PdsdZxXBwliQB7jjb46M37duU9l1No5iDsyQJgBmNM1Ifamy73H1sx1kaCQdnSRIxRvb27E19qLHtcveU+in5v/Tp3uc0Ojk4S5IAd5zt8dHHdpylkXBwliQB0FDdkPpQY9vl7mM7zr44UCPh4CxJAmB6o+dxtsd+H+w7mPZdTaOYg7MkCYB9PZ7H2R77fWzH2WecNRIOzpIkwB1ne3z0ihkr0r6raRRzcJYkAZ7H2R4fvWlvfsfZt9zWCDg4S5IAz+Nsj492x1nFcHCWJAF4Hmd7XPSUBnecNXIOzpIkYozuONvjoldMd8dZI+fgLEkC3HG2x0c/tu8xwGecNTIOzpIkAGY2zUx9qLHtcvfBXnecNXLVad8ASVJl2NO9h9qq2ooacmy71D21YSpbj26le6CbjoGOtO92qjDVoZrGmsaTf/0M3hZJUgXrG3bH2R77PbtpNhv2bGD1t1anfZdTBQoEnvyDJ0/6dQdnSRKRSH2VO8722O+vPP8V5k+YT2ttK8umLuPpA09zuP8wk+om2eO8F7YuZN0L6xKPle44S5IAd5zt8dG7u3ezYsYK3rfyffQN91FXXcfrZr7Otukf7j/lcdJnnCVJAOzt3uuOs23b47b3dO855XHSZ5wlSQD0DvdW1IOYbdv2mezZLbNPeZx0cJYkEYk0VDVU1IOYbdv2mezfWvhbpzxWOjhLkgB3nG3bHt/97Ze+fcrjpDvOkiQA9vTsoaaqpmIexGzbts9k7+zaecrjpM84S5IA6B1yx9m27fHb81rmnfI46eAsSQKgododZ9u2x2+/8+x3EgiJx0kHZ0kSMUZmNrrjbNv2+O5MSB6N3XGWJAHuONu2bZ9qcPYZZ0kSAH1Dfak/aNm2bafZVZmqxOOkg7MkCYD66vrUH7Rs27bT7Ak1ExKPkwWtaoQQpgP1xzrGuK24w7MkqZJE3HG2bdue3zo/8Vh5ysE5hPA54ApgFxCACPxqKQ7UkqTKsbdnrzvOtm2P+05SyDPOrwcWxRizJTkyS5Iqkudxtm3bvjLxOFnIjvMWTljTkCSNTZ7H2bZte13icbKQZ5znAe0hhC35jjFGVzUkaQyJMTKzyR1n27btJIUMzteU5rAsSapke7r3UJ2pTv1By7ZtO81OUsiqxjDw98C3gP8Op3gvQknSqOSOs23bdvE7zl8A/gV4M3AfcHfxh2dJUqVxx9m2bTt5x7mQwbk+xvj1GOORGOMDQE1JjtCSpIpyVtNZFfGgZdu2nWYnKWRwrg4hLAfI/29M+uYQQk0I4V9CCA+FEDaEEN4RQrg8//HaEHJvAh5C+EwIYUFBR3NJUllFIru7d1fEg5Zt23aanaSQFwf+X8A9IYRZwE7g+lN8/3uAgzHGa0MIU4DHgSeAS4GPAOeHEIaAjhjjSwVcvyTpDHDH2bZtO3nH+ZSDc4zxceB1p3Hs/TKw9oQeArqApvyvbuBDwHtP4zIlSWXmjrNt2/Y62ma0nfQ4edLBOYSwNsZ4VQhhNy+vZwRy53GedbLfF2Psyv/+FnID9F+Te9b5DmATcDawHrgmhHABcF+M8ZGT3IbryT/DPW/evJP+EJKk4rnjbNu2PcLzOMcYr8p/+PoY4/FLCSGce6qDbwhhLvA14HMxxi/mP/3uEEIVcD9wHXAPcDXwdeCKk9yGu4C7AFauXJm4Wy1JGrljO85VmarUH7Rs27bT7CRJzzgvA2YDnwgh/CW5Z5szwN8CFyT8vhnA94AbY4wP/tKXrwfuzX+cIfdMdlPiLZQknRHuONu2bY98x3kS8HvADGB1/nNZ4HOnOPa+P/97PxBC+ED+c28jdxq7S2KMqwBCCHuAhwu4PEnSGdBY3VgRD1q2bdtp9oh2nGOMDwEPhRDaYoybCj3wxhhvAm56lS/1AqtO+L4bCr1MSVL5ndXsjrNt23aSQk5HNyeE8HFyzxgHYGqMcXkJjtGSpAqyu2s3mZBJ/UHLtm07zU5SyBugfBD4MLCd3Ftu/6zoo7MkqaLEGOkZ6qmIBy3btu00O0khg/PBY6eLizHeCySP4pKkUckdZ9u27XWJx8lCBuf+EMJFQE0I4TLgrFIcoCVJlWVW86yKeNCybdtOs5MUsuP8p8C5wMeA28mtbkiSxphdXbsIIaT+oGXbtp1mJ0k6j/OSE/LY+H1bCY7NkqQKE4mex9m2bXvxyM/j/I8nPb7Cb4z04CxJqkwN1Q0V8aBl27adZo/0PM6/XpYjsySpIrnjbNu2XeSOcwjhRXLPMh9zNMZ4YZHHZ0lShdnV7Y6zbdt2kkJeHHhu/n8DsAK4ushjsySpAvUOuuNs27ad5JSno4sx9ud/9cUYHwZOvvghSRqVYow01LjjbNu2naSQVY2P8/KqxiwgW/whWpJUaWY3za6IBy3btu00O0khqxo/P+HjnwHfKebALEmqTLu6d0Eg9Qct27btNDtJIe8c+GVgMvBGYBrQU/zhWZJUaXoGeyriQcu2bTvNTlLI4PxFYAa5Z5rnAf9c/OFZklRpGmsaK+JBy7ZtO81OUsiqxpQY4635j9eFEB4q+ugsSao4s5o8j7Nt23aSQgbnzSGEN8cYHw4hLAfaQwg1QIgxDpTiYC1JSp87zrZt28Wfx/ktwGUhhEGgJv+558idaWNRUUdpSVLF6Blyx9m2bTvJKQfnGONrQwiB3AsDD8QYPR2dJI1BjdXuONu2bbfNOPlblpzyxYEhhEuAF4DvAltDCL9ZsqO0JKkiRCKzmt1xtm3bTlLIqsbHgF+LMe4KIcwGvgr8rxIcpyVJFWRX1y7AHWfbtsd3JynkdHTDMcZdADHGnUBfsQdnSVLlccfZtm27+PM4d4QQ/iyEcH4I4c+AQyU5QkuSKoo7zrZt28nncS5kcH4PuTc++RgwF/ij4g/PkqRKEmNkdvPsinjQsm3bTrOTFHJWjaMhhPXAQeDpGOPhEh2nJUkVZFfXLiIx9Qct27btNDtJIWfV+CdgFdAL/H4I4VOlOURLkiqJO862bdvF7zgvjzH+Xozx0zHG3wXeVJHjFbIAACAASURBVJIjtCSporjjbNu2XfyO85YQwkKAEMJ0YFsJjs+SpArjjrNt23bx53F+I/BsCGEbMAfoDyHsBmKMcVbxh2pJUtoikZ1dO8mSTf1By7ZtO81OUsiLAxeX7MgsSapY7jjbtm0Xv+MsSRoH3HG2bdsufsdZkjQOuONs27advON80sE5hPDP+f+9ocTHZklShYkxt+NcCQ9atm3baXaSpB3nC0IIfwdcHUKY/0sH2PeX4DgtSaog7jjbtm2PfMf5XcAT5N745Be/9EuSNMa442zbtp2843zSZ5xjjC8BL4UQfgxMAH4FeD7G+EQpD9SSpMowp2VORTxo2bZtp9lJCnlx4DuBu4E3A3eFEP6iBMdnSVKF2dG5oyIetGzbttPsJIW8Acpq4NdijEMhhBpgPfD3JThGS5IqRCTSO9RbEQ9atm3baXaSQp5xDjHGIYAY4yAwWIJjtCSpwjTVNFXEg5Zt23aanaSQZ5z/dwhhLfAQ8GvAwyU4PkuSKsycZnecbdu2kxTyltt/EUJ4O7AUuDfG+O8lOkZLkipEILCjawdDcSj1By3btu00O0khzziTH5YdmCVpjIpEegY9j7Nt23aSkr/ldgihJoTwLyGEh0IIG0II7wghXJ7/eG0IIZP/vs+EEBaU+volSSPjjrNt23aRO84hhDkxxh0n9GtijElvgvIe4GCM8doQwhTgcXJvpHIp8BHg/BDCENCRP1e0JKkCuONs27Y9wh3nEMIyYDbwiRDCX+U/XQV8HLgg4TK/DKw9oYeALqAp/6sb+BDw3lMcwyVJZ5A7zrZt2yPfcZ4E/B4wA7gm/7ks8LmkC4wxdgGEEFrIDdB/Te5Z5zuATcDZ5M4FfU0I4QLgvhjjI692WSGE64HrAebNm5f4g0iSitM92F0RD1q2bdtpdpKkt9x+CHgohNAWY9x0OgffEMJc4GvA52KMX8x/+t0hhCrgfuA64B7gauDrwBUnuQ13AXcBrFy5Mp7ObZAkFS7G6I6zbdv2C+tom9F20mNlIWfVmBJC+BZQf8IB9jdO9s0hhBnA94AbY4wP/tKXrwfuzX+cASK59Q1JUsrmtsytiAct27btNDtJIYPzp4CbgeRLetn7ya15fCCE8IH8594G1ACXxBhXAYQQ9pB7M5XE1Q9J0pmxvXM7g9nB1B+0bNu20+wkhQzO22KM3y/0wBtjvAm46VW+1AusOuH7bij0MiVJ5ed5nG3btke443yCfSGEO8m9wC/C8d1jSdIYEYk01jRWxIOWbdt2ml3sjvOL+f+dWZKjsySpIs1tccfZtm07ySkH5xjjR0IIbwUWAo8Cz5XmEC1JqiQ7One442zb9rjvJIW8c+B/BeYAS4EB4DZePq+zJGmM8DzOtm3byTvOmQKOpb8WY/x9oCvGeB+5Z54lSWNIxPM427Ztr3thXeKxspDBuTqEUA/E/BuYDJfiIC1JqixzWuZUxIOWbdt2mp2k0PM4PwZMI7fj/KniD8+SpErjjrNt23aRO84xxi+HEH5C7qwae2OM20p0jJYkVRB3nG3btovccQ4hfBC4Ocb4U+AfQgi3lOgYLUmqIO4427ZtF7/jfGWM8X0AMcargXeU4PgsSaogMUbmtngeZ9u27SSF7DhnQwi1McaBEEINhQ3bkqRRZnvndgayA6k/aNm2bafZSQoZnD8PPB1CeAo4F/hECY7PkqQK446zbdt28o5zoW+5/WZgEfBCjPFACY7PkqQK446zbdv2OtpmtJ30OFnI4PyRGONFwP6SHZ0lSRUlEpnXMq8iHrRs27bT7CSFDM4xhPA14BdAFiDG+P7iD9OSpEoRCGzr3EZ/tj/1By3btu00O0khg/M9pTksS5IqVSTSM9RTEQ9atm3baXaSQs6Q8W9ADbkd53bg34s/REuSKk1jdWNFPGjZtm2n2UkKGZzvBOYBlwItwP8o/vAsSao08ya442zbtp2kkMF5cYzxg0BfjPEbQGspDtCSpAoSYVvHtop40LJt206zkxQyOFeHEKaSe5FgC/kXCEqSxhZ3nG3btovfcf5r4GFgJfAT4KPFH54lSZXG8zjbtm0n7zif8qwaMcYfhRBeC8wCtscYY4mO0ZKkCuJ5nG3btovccQ4h/A7wPPAA8HwI4TdLc4iWJFWKSGRbpzvOtm3bSQo5j/MHgDfEGPeFEGYA3wD+VwmO05KkCtI92F0RD1q2bdtpdpJCdpwPxhj3AcQY9wIdJTg+S5IqjDvOtm3bRe44Ax0hhO8CPwJWAI0hhP8KvvW2JI0l81vmV8SDlm3bdpqdpJDB+cTRe2dRR2VJUsVq72ynd7g39Qct27btNDtJIWfVuK9kR2VJUkWKRHecbdu2Fxe/4yxJGgfccbZt207ecXZwliQBMH+CO862bdtJCtlxliSNA+0d7fQOueNs2/b47iQ+4yxJIkZ3nG3btq9c7I6zJKkAzTXNFfGgZdu2nWYncXCWJAHuONu2bW/vdMdZklSA9o52eoZ6Un/Qsm3bTrOT+IyzJAmArsGuinjQsm3bTrOTODhLkohEd5xt27ZfcMdZklQAd5xt27bdcZYknUIguONs27bd4o6zJOkUIp7H2bZt+8rF7jhLkgrQVNNUEQ9atm3baXYSB2dJEgALJiyoiAct27btNDtJyQfnEEJNCOFfQggPhRA2hBDeEUK4PP/x2hBCJv99nwkhLCj19UuSRualjpcq4kHLtm07zU5SjhcHvgc4GGO8NoQwBXgceAK4FPgIcH4IYQjoiDG+VIbrlySNQPdgN20tbak/aNm2bafZScqxqvFl4AMn9BDQBTTlf3UDtwKfKMN1S5JOUzZmOdR3yB1n27btF5J3nEv+jHOMsQsghNACrAX+mtyzzncAm4CzgfXANSGEC4D7YoyPvNplhRCuB64HmDdvXqlvqiQJ2Nm1E4A5zXMq4kHLtm07zU5SlvM4hxDmAl8DPhdj/GL+0+8OIVQB9wPXAfcAVwNfB654tcuJMd4F3AWwcuXKWI7bKknj3eDwIAD92f6KeNCybdtOs5OU48WBM4DvAbfEGO/5pS9fD9x7wnVHcusbkqSUDGZzg/OhvkMV8aBl27adZicpx47z+4FJwAdCCD/M/2oIIUwALokxfiPGeBjYAzwM3F2G2yBJKtBwHAZgWsO0injQsm3bTrOTlGPH+Sbgplf5Ui+w6oTvu6HU1y1JOn1D2SEAfnXWr1bEg5Zt23aanaQsO86SpNHj2OD86O5HGcgOpP6gZdu2nWYn8Z0DJWmcOzY4H+g9UBEPWrZt22l2EgdnSRrnhmJucJ7RNKMiHrRs27bT7CQOzpI0zh17xvmSOZdUxIOWbdt2mp3EHWdJGueODc4P7XyIrsGu1B+0bNu20+wkPuMsSePcsdPR7evZVxEPWrZt22l2EgdnSRrnjj3jfFbzWRXxoGXbtp1mJ3FwlqRxLhAAuHz+5RXxoGXbtp1mJ3FwlqRx7qI5F3Hd8utYv3t9RTxo2bZtp9lJHJwlaZxrrGlkZtPMinnQsm3bTrOTODhLkuge7K6YBy3btu00O4mDsySJppqminnQsm3bTrOTODhLkpjXMq9iHrRs27bT7CQOzpIktnVuq5gHLdu27TQ7iYOzJMkdZ9u27XwncXCWJLnjbNu2ne8kDs6SJHecbdu2853EwVmS5I6zbdt2vpM4OEuS3HG2bdvOdxIHZ0mSO862bdv5TuLgLElyx9m2bTvfSRycJUnuONu2bec7iYOzJMkdZ9u27XwncXCWJLnjbNu2ne8kDs6SJHecbdu2853EwVmS5I6zbdt2vpM4OEuS3HG2bdvOdxIHZ0mSO862bdv5TuLgLElyx9m2bTvfSRycJUnuONu2bec7iYOzJMkdZ9u27XwncXCWJLnjbNu2ne8kDs6SJHecbdu2853EwVmS5I6zbdt2vpM4OEuS3HG2bdvOdxIHZ0mSO862bdv5TuLgLElyx9m2bTvfSRycJUnuONu2bec7iYOzJMkdZ9u27XwncXCWJLnjbNu2ne8kDs6SJHecbdu2853EwVmS5I6zbdt2vpOUbXAOIbwhhPDD/MeXhxA2hBDWhhAy+c99JoSwoFzXL0kqnDvOtm3buU5SlsE5hPBXwD8B9flPvRe4FNgJnB9CWA50xBhfKsf1S5JOjzvOtm3buU5SrmecXwB+54TuApryv7qBW4FPlOm6JUmnyR1n27btXCcpy+AcY/wKMHjCp24H7gBeBM4G1gPXhBDuDCG86WSXE0K4PoSwMYSwcf/+/eW4qZIk3HG2bds+1knOyIsDY4zPxhjfDfwtsAb4InAZcCPwgYTfd1eMcWWMceW0adPOxE2VpHHJHWfbtu1cJznTZ9W4Hrj3hOuO5NY3JEkpcsfZtm0710nO2OAcQpgAXBJj/EaM8TCwB3gYuPtM3QZJ0qtzx9m2bTvXSarLdRDOnzHjjSd0B7DqhL6hXNctSTo97jjbtm3nOolvgCJJcsfZtm0730kcnCVJ7jjbtm3nO4mDsyTJHWfbtu18J3FwliS542zbtp3vJA7OkiR3nG3btvOdxMFZkuSOs23bdr6TODhLktxxtm3bzncSB2dJkjvOtm3b+U7i4CxJcsfZtm0730kcnCVJ7jjbtm3nO4mDsyTJHWfbtu18J3FwliS542zbtp3vJA7OkiR3nG3btvOdxMFZkuSOs23bdr6TODhLktxxtm3bzncSB2dJkjvOtm3b+U7i4CxJcsfZtm0730kcnCVJ7jjbtm3nO4mDsyTJHWfbtu18J3FwVkGyPb3E/n4A9nX0MTCUTfkWSSold5xt27ZzncTBWQXp+tI6ur72bQBW3fUTbv/mMynfIkml5I6zbdt2rpM4OOuU4tAwsaOL7P5DxBg5Z3ozX920g77B4bRvmqQSccfZtm0710kcnHVKwwcP5T7IZhk+eIhLXzuD7oFhvrt5T7o3TFLJuONs27ad6yQOzjql4V0vD8hDL+7gwrkTmdpcy9rHdqR4qySVkjvOtm3buU7i4KxTGtq1N/dBCAxt30lNVYbpLXU8vOUAezv60r1xkkrCHWfbtu1cJ3Fw1ikNHzgEVVVQXU12/0GGs5Ga6gzZCF97fGfaN09SCbjjbNu2neskDs5KFGMkdnRBTTWhrpbY1w99fZw7s4XXzGjh/o3biTGmfTMlFckdZ9u27VwncXBWotjRCdksoa4W6usAqN67j6tWzKWlvoqt+7t5csfRlG+lpGK542zbtp3rJA7OSjS0O7/fXFubG56B2t17WPtY7j9lVGUCX9nkiwSl0c4dZ9u27VwncXBWoqEduTNqhNoaQghQVUVm337aD/aweHozb1g4mQce30n/kOd0lkYzd5xt27ZzncTBWYmG9x+EqkxuaAaoraGmq4uFk+u5asVcqqsCHX1D/Mez+9K9oZKK4o6zbdt2rpM4OCtR9shRqKo+3qG+jkBk9aIW1j62nf7BYRprqzynszTKueNs27ad6yTViV9V2QwfPkLvd39EzGbTvikJIgwMEpqb6P6Pn5JpqKf+9b9CBH7xxBbah1pZMLWJGRMa+OaTu/jST7fxtuVnMaG+Ju0bLuk0ueNs27ad67YZbSc9Vjo4p6R/09MM792fOz9yJauqgsYGur+1nkxDHQ1vWk42k2Fhx37mzz2Lq1bM5b71L1JfU8UtX3mKv37gad66dAbvunA2l7xmOrXV/kcNaTRwx9m2bTvXSRycUzK0fRdUVVF11vS0b8opDXd0E7t6Ge7qJdvTR1VjA2d3dXPNaybx/z22nQNdA/zWeTO5YO4k/vnhl3jw5/v49tN7mFBfzW+dP4t3XTibFfMmkcmEtH8USSfhjrNt23aukzg4pyAODhGPdkBjfdo3pSCD23Yf/3jghR3ULl1A7Opm50+epD07hflTGrlqxVzWPradKc21XDC3lbOnt/Bvj7bzpZ9u54uPbmPWxHp+58I5vPPCWZw9vSXFn0bSq3HH2bZtO9dJ/O/oKRjasQuA0NCQ8i0pzNDW3O0lwODP28lUV0N1Na/t3s/8yQ3Hh+b2gz3Mn9LI775uHlsPdDFrYgNXXnAWN/7G2WSz8NkfbOGtn/wxV9zxEP/00Fb2dfSl+4NJOs4dZ9u27VwncXBOweDzLwIcf0ORSje4fS+hsZ5MSxODL+4EINPcyCSGuXZe/X8amn95iL7m9fPZ39nHgqmN/PYFZ/GHv7qAPUf7+Ni/P8sbP/4g7/mnR/nKYzvo6h9K+aeUxjd3nG3btnOdxFWNFAzt3A1VVYTM6Ph3y/CuA4TGekJtNYPb9xGzWUJTI8NHOzm8aTPtmVmvOjSfrJfMaGZSYw2NtdV8d/Me/veWA7z/a09x6a/M4F1ts3nLOdOoqRodfzbSWOGOs23bdq6TODifYXFggNjRBY2jY00j9g8wfPAo1fNmQl0t7DrA4I791M6bQaa2lnP7j3D2rIVcWeDQ/Mv92lkTaKqrpjqT4XvP7OUbT+5mYkMNV14wiysvnM2Fcye+/OYrksrGHWfbtu1cJ3FwPsMGtx3bbx4lLwzckX9HwMYGMk25YX/wFy9RO28GoaWJ2v5+rp0O94xgaG4/2MOCqU3H+/y5rdRWZRjOwr8+uo37Hmln7qQGfqdtDu+8cDYLpzal+CchjW3uONu2befa8zhXkKEt+f3m+rqUb0lhBl/MDfpVTY1QV0Oor2Vwy3b4zTcQ6moZzmTo3/wL2qsXnPbQ/Mu98IQhejDbSiDQOzDMpx98nk8/+DznzWnldy6czW+dP4upzaPjz08aLdxxtm3bznWSsiyShhAyIYQ7QwiPhBB+GEI4O4RweQhhQwhhbQghk/++z4QQFpTjNlSqoZ17oLpq1KwfDLXvhtrq3NAcAqGlkcH2PQCEEKhqqGf+YDfntVYVNTT/ci+e1sxfXX4ur509gbZ5Ezl/TitHewf58Dee4fV/833+4J4NrHtiJz0DvqhQKgV3nG3btnOdpFzPOL8TqI8xvimE8EbgH4AAXAp8BDg/hDAEdMQYXyrTbag4sa+f2NVNaGpM+6YUbGjXATJNjccH/UxTI0Mv7WL4aBdVrc3Q3AjdPaxqHeCuEg3Nv9znzGg+3q0N1QwORZ7eeZQfPbef+poMl792Ju9qm8ObF0+h2hcVSiPijrNt23auk5RrcP414DsAMcafhBBWAj8CmvK/uoEPAe8t9AIX1GVgT/LT5xWhaQK0tAKQ7e0j9r18ruKh9h25DxrqiTGSPXiUODScxq0s2NDeQ1TNmHK8Q0tuz3jguW00vO5XyNTUMFRdTdXzW2mvPZv5J6xblGOIXnrWBK5aMZcvb9zG0zs76BvK8r1n9vLAE7uY3FTLlRfM4rfOO4uJjYWd6m/+5EaHbQl3nG3bto91GjvOE4CjJ/Qw8HHgDmATcDawHrgmhHABcF+M8ZGkC1y7dDL89R+X6eaWUHU1fHot1NXT+U9fhGz2Fd8S6mrpe3QzHfd+M4UbePoyjfVkDx8lhkBmQjNUVzG8a//LX29qYNLRTtqm1PHrZRyaT+xth3pZPqeVq1bM5Us/3caTO47S3T/EvzzSzj8//FLBP9tF50zlvj96/ahZnZHKxR1n27btXCcp1+DcAZz4vsqZGONTwLtDCFXA/cB1wD3A1cDXgSt++UJCCNcD1wN8c/Vc3r7gN8t0c0tkoB8OH4DNj0Hbm6maPplsTz9kThjKanK7wv0/ew5qq6leMDu921uIEMhMbiUe7YIIIZOh/nVLqVtx7svf09RIAG668gKe2nmU6S31VIUM72qbfUaG6B2He7lw3kSuWjGXLz7aztM7O5jSXMsbFk7h0RcPcqCrn6nNda/oKU11/PtTu/n+s/v4zV+ZkdofsVQJ3HG2bdvOdZJyDc4PA78N3J/fcX7qhK9dD9yb/zgDRHLrG68QY7wLuAvgv1/TGJk45dW+rXJks3D4IPz0x9D2ZkJdHYFAqH7lH/Pg1p1kWpupnjY5hRt6+mIIkM0SYyT2DFDVWJd7I5RMJvdsbXU1dTVVrFwwmZULJtPVP8Rj7YdoqKku69D8y737aB9t8yceb4AV8ye9at//02001lbxsW8+w8VLplFb7cqGxi93nG3btnOdpFyTwteAvhDCeuBTwJ8DhBAmAJfEGL8RYzwM7CE3ZN9dpttxZmUyUFsLv3gy8duGDxwhe7SbTEvzGbphpRBzA/LgEAxnybS2Qv/ASb+7ua6ai5dM58PveC1/cdlrONDZz9AwZ3SIPlVvP9zLivkTaT/Uw7892n4G/yylyuOOs23bdq6TlOUZ5xhjFviTV/l8B7DqhL6h0MscNSuoTS25dY0jB0/6LX1PvQBAmDB63tAjDmepu+Bc+p96nhAgM2UKw7t3FfR7pzXX87blZ3H5spnsONzLoy8epKN3qCKG6He3zeG5vV188n89x7sunF3wiwqlscYdZ9u27VwnGTX/bXq0zM20TMz976aHT/otg8++SKirITNa3nY7RoiRxl9/PQzlzpscmpsItbWndVaQEAJzJ+eG18+ubuO6X1vEs7s72NvRn9oQ/ZVNO5gxoY6uviH+3//YUq4/QqniueNs27ad6ySjZnAeNeobcisbj/3vV/1yjJGB/H7zqDmTw9AQmQnN1P3K4uP72iEEas5ZCAMnX9dIkskEzpnRwu+/aQH/eO0K3vPG+WxqP8yOw71nfIheetYEfmPpdO5b/xIvHugu5Z+cNGq442zbtp3rJKNmcB4tMyYhQF0DvPgLiPEVXx7ac5DY1Xv8fMijQRwcpmpyKzWL5+ZeIJh/lrlm0XxK8d8C6qqrWD57IjdcvJjPv2cFq1bO5eEtB87oM89NdVUAfPxbzxb980ijkTvOtm3buU5SrrNqjG/NE2D/bkLHAahv/U9fGth8bL95FL0wcGiYqmmTCDXV1CxZQN9PnwYg09pCZlIr2Y7Okl3VhPoa2uZPpm3+ZPZ29PHigW6+/8zesg/R+zr6WXpWC997Zi8/2XqQNy6q8DO4SCXmjrNt23auk4yaZ5xHlfw7B1bt2fqKLw38vJ3QUEemof5M36oRi0NDx989sP71y//Tm7rUvGYxDJfn3Q9nTKjnjYumcOvbzuVv3rmcd5w/i39/clfZhuhb3nYuTbVVfPSbz5DNvvK/FkhjmTvOtm3buU4yap5xHjWrGgA1tVBdQ9X+dlhw4fFPxxhz52+e0DR69psBhoapnjkNgLrl5xDq645/qWbebPof3VTWqw8hsHh6M4unN/PGRVPZvOsoR3sHeeDxnSUfos+aWM8zuzr42uM7efeKOWX9uaRK4o6zbdt2rpOMmsF51KlvpOroXhgeyr0NNzC0cz+xp58wa3rKN+40VWWompo7W0jtuQvJNL78bHmoq6V6zlkM7dh9hm5K4Lw5udvyugWTeXzbEQazWb7y2I6SDNFt83KX/Ynv/Jy3LZ9JY613EY0P7jjbtm3num1G20mPlaNmKlg0YxgyPZBtTPumFKalldB1lMz+7WSnzgNg4MnngdxucGCIxrCP7ngWlX6yvZDJULPnWfj096kCpsztpvrf74ZHcu+qXtc/SPXRbvj0E2f0dtUDb8p//MbhyP7+SKankeefHubg1Dczf+bUEQ3RB7oG+I+f7+eP/8dG5k0+9d+3qkzghosWM7eA733khYN8/Wc7qauu4oaLF3FW6+g4JaHGPnecbdu2c51k1AzOV6wYgPg56JoGfefC0DLIVvCZKZpbiCFQ17GTwbbceFc14yWqZkwmU19HY9jJxCnddB/IAlXp3tZTCYHax74Dg/2QyVBfFwm7noPduYE/ANUhA/uzyZdTRrXA7PzHM7PDvGFgO0eu+QeyVTXc/NYlAGQCZCPHOwBf+PIG2rO1/2mI7hkY5tyZzWze1cHj246QzUYymUBtdYaBoewrundgmP7BLH939fmJt3FwOMv/ff8T7OvsJ8bIz3Yc4cs3vInqKl9qoPS542zbtp3rJKNmcP4/Pl/Pv/3pJGg4DC0/hvhj6J4GvceG6Ao7S0WmilDfSPXOn1P9pjaoroGaBnrXPwVAfU0vA92RbM8gmcbKHpwzYZgw2AeTpsL0Wa94RWmlPV8eDh+gbu92Znzxk/B/fihxQf51Dz5I11XvfsUzz++79DUFP1O9v7OfdT/bxfuvWMqkppO/8+BXN+1g99E+3nLOFC6cO4k7/mMLd/7oBW78jXPK8ccgnRZ3nG3btnOdZNQ81fWLvRmY/ho46w0wcQWE2VDfCdMegpmfh9Z/hrr1kCndqdGKNmkqDA2+yrsIZqltzjLYV0Xs7UvlphUqxkh1df42NlTYP05OZtJUaJ0MT/wE1v1L4rcu6z3C25fPKuqFhX96yWIGhrJ8+bGT/+edweEsdzy4hclNtfzZb5zDno5epjTV8qnvP8+TO46U+k9AOm3uONu2bec6yagZnI8LAWr///buPTius8zz+Pc93ef0Xbdu3WVLsizf73acGIckJBMSCOBwyUAxQDFbDLAzs8NsbQG1Myws7MyyxcxQBQyBAMPCQCAEkpCQWUjCJeTqOI7tJA4ktmPLsmTL1l0tqe/97h+nZUt2q3Vrubvl51Plcv+6T7de9dHRefrVe943ADXt0HANVOwAoxHcYxB6Guq/CRXfBddTYAwXtq3+MnsVwd88NOVu04hgGGBUlqOMYuuvvUhaY7kz0815SmR8OUBto93eh38E+7Ov4gh2b3lzlZfO/vmvWPh8xwBlbifff+YkqWmmsfv5wW66hyL8xRtbue9AF50DEa5bFaLc7eQT9xwiEl+cKf2EmC0Z4yxZsmTJds6l9Arni1l+qG6Hhquh4iowmsCKQOhZqP8WVHwHrCfAGLz8bVMKvAE4/iqMj56/26UH0RrMmgoo9umCk0lcvjQYjvOzg5QEpaCp1R4i8+0vQteJaTdtDvm4bVP9gmbjWN9YRvdQhN8fOXfJ6ydTab7222O0BL2c6Bs9//z3X93MpmXlnOgb44u/OhV4GAAAHNZJREFUlBULRWHJGGfJkiVLtnMupV84T2b5ILQSGndC5VVgLAMrBtXPQf13oPLb4HocjIHL16bKEOg0PPnI+btcVpRUUuEo96BcFjqRvHztmSOdTGG6EmCahW7K3BkOWLbCXvr8nz4F4en/AvG2jfU8uIB5of/mxna8loPvPXPyktd+6MXTdA6M01jpoXNgas92NJFmVa2ff3/2JL8/0ruY74YQOckYZ8mSJUu2cy5Lq3CezPRBqC1TRO8EYzmYCQg9D/X/BpXfAtdvwdG/uO3weMHhhCd/CYBS2u7BtTwopbA2tqPHi3ecs04lcDqT4CrRadMsFzS0wFgYvvRJSGb/kFLhs9i6vHLewzV+fqibKp/FE0d66egbO/+6qbTmq785SqXXJJlKZ33+596+nnKPyX+79xCDY/HL9MYIMZWMcZYsWbJkO+eydAvnyUwvhFZA41VQeTU4msFMQugFqPsuVN0Frt+AYxF6/JSyl+Du6UKFBzCtKMoAI2gvtOG+eiM6kcj/180T04jZk1J4i3jqv5n4/FDTAGc64a7/bfdAZ/G2zfXs2dy4oMVTHErxw70Xep0ffuk0Hf3jhPwuWkK+rM9/6MVulld5GBiL89/vfxk9TfuEWEwyxlmyZMmS7ZzLlVE4T2Z6INhqF9HBa8DZAs40BA9A3fcg+E1wPwbOc+RtAHJFCADH4adwGWG0BiOQWTxkbRvKWbzT0VnnZ9Qo4cIZ7CEzZZVw8Bn4xd1ZN3Fl9sN8h2t8cFcLDZVu7nn+FJF4ilRa85XfHMVnOdi6vDzn89c1lPH+ncv51Ss93Heg+3K+M0IAMsZZsmTJkidyLiV0tdcicLihqgVogVQMRnog0QdVh0AdgqgfxlZAYhOk6pj3jMUuF5guHMcO4Q6kSKUMnIZdpJntzehkGq01Ksd8w4ViuVNorVDm9PMTl4y6JojH4KEf2hcObtt9ySa72oI8e7yf61ZVz6vnOeBycmogwoOHuvG7nRzvHWNXWxV37Fg+4/Pv3d9JmdvJZx88zNWtVbNaiVCIfJExzpIlS5Zs51yuvB7n6ThcUNkMjduh+g1gtoJDQdVLUPdDCN0J7l+C4zTz6okur8CIjeIuA9wXCiLD48JsaUBHYnn7VvLJcifRypFzEZGSoRQsy8y0cdcXofvSC/mUUty+df7DNTY0lrG8ysv3nungK78+SrnH5BM3rZrV809lpqhLa83f/uTQtFPbCbEYZIyzZMmSJds5lyu7x3k6hmUX0ZXNkI7DyFlI9kLVYVCHIe6B8ApIboJkI7Pqia4IovvOogBHqMq+TyUgEca1cwWJn54Cr3uODU0vcj2rMT1ptFmiFwZmMzHTxsmj8KVPYhhBiI5P2aQtYNDkBqvcwZ5Ntfw0U+SuqHLzrk11PPh8Bz0D47RXedmzqY4Hnu/g5GDsfBF8vHeM/SftRU0+cVM7DxzsulAkb2vioec7OD0QoTnkz1pEnwvH2HdikG88fowP727N67fvdhqyxLfIymX4ePOyt/Oz135BV/gMTYFm3rzsNsklnG9tfltRFCGSJZdazkWVyoVIO1b59P47dxa2EekEhM/CWC84RkBp6N8I0Vtn9fTUa69i6Dhq1QawjkLNQ+dr7mTYRd+vbiExVDWr1zLNOLVN/ajLUAOlK2owausW/wtdTmPhnHM7T9DAT3Z/jNiy1bz9j8/hSGdfqOTnFa2su34rP3vhFMd7x3jx1DAhv8Xu9iCnJk1BF370CbZH+kgBXW+8gbtPjF3S89zRN0b3UJSuwUh+v2fsH7dyj0l1wEVNmYtqv4vqwIV/oYnsd1HptTCKfYEekRfD4wmu/uJjRBOlcT4Qs6OUZtvWx9nQJBd+SpY8l/z5N3x+2pOf9DjPhWFCeZP9L52As4eh/FWI3gzMfIGf0dyCjkZRhgHmH0Eb4G1Ea3Ckz1B7x69Irv0xumzHzG0JD5G663/ZU90tIuUwcFTXLOrXKAhfAJpWED3eg7tu+gsf1UAv70u8hr5xD+GOQ5kPOlOPp9R4hFvT/Xx5fycnByKsqPaxZ0sjTx3rnVI0//q5o3w80gemiTJNfnsmknX4RkvIx1+9qZ2v/OYIveEYlT6LzU0VvNg1xOBYfEG5fzSOy2lQ4TV5rSfMgdgQiVSaZJZhIYaCKp9FdcBN7URhHbhQbIcmFd1lbmdRjtEXs9M3FiOa0IT8Fo2Vnrz9vEkuXN53YoBXe8J4Wc6etmuLqiiRLLnYcy5SOM/XRBE9+gqYRyGxZsanKLcb5c4Mx/CehlQAAm12Geath8GDmK++D274BdTfkvvFxqugJmgv6y3mx+fHvXFl7m1GR+D1P6IMA+WywOO+tEA0DNwjIzii/TTXhM4XwUPjiSlF8Rt77DHVKliBM5Hk3VsbORfr4p3bmrKOeTYdBrvaguezx3SwZoF598oL2WEomoNe3r2tiXue7+T13jGCPostyyr53WvnODcSxXIaeEyDl7qHGY8niSfTZBt6bToUQZ/di10TcGd6ra2svdleS37tFJuJ8fSrav3815tX5+3nTXLhstu0/xy5pXpbwYsQyZJLLW+r3Tbt70s5gy2EJwjDTrBemlXhfJ4RBveYvSjLBKcXKrfD0AF4/DbYfS8sf1f+2yzmxu2FgXMQm36RGuX3khoZ5TY1hLV9a9YieLR3iN2MYHjcGE4nOpGkscLDu7c3ce/++V2ImM98eijKmrrA+exyGlzbfuFDgOU0aA56ede2Jn687yTHe8ep8plsaqrgiSO9nB2J4XIaKOCFkwOMx1PEkums75fHdBD0W9SWuanJMkRkomc75LfOTxEoFlciZe+r3SvnN5uM5OLLTZUeDp0a5oWeg7iriqsokSy52HMuUjgvhDKASgh0wXgc9CynbHO+bv9fFrrofrddPA8ehKfeA9d8D1Z8KJ8tFnPlL4ORQTj2h2k3UYaB4XGxLjrE1/Yd5+RQ4pKT2MfUAApQ5YEpz922vJJHXjlLc5CiOunmyj3DMdbWXyiy3aaD61ZdKLI9loPmoJd3bm3iR/tOcqJ3jEqfxcbGcp482su5cAxDQTSR4pnX+4nEU8RT2YvsgNtJtT8zHjvgptrvIhSwpo7N9ruo8lly0eMCJFN2j/Pe430kUrqoft4kzy/ftKaWh1/qoT8yxDVFVpRIllzsORcpnBeqoh7CvfaY5fjm2T3HeRxSTjADlz7mcEHVdhg8AHs/DIkRWP3XeW2ymIOJFRNf3AtMP9ZbBfwYkSjNZ7sYa2idchLbWO5gy9kBlNuF4Zx6yCml+Pj1bRzpCRfVSTdf+dxIjHUNZeez13Jy/aoLOeB20hz0smdLIz9+7iQn+sep9JqsbyjnqWN99IZjxFNphsYT/OH0COPxVNbx2Aqo8GYuegxc6Mm+eCx2yO+iwmPKRY8XmXhPe8MxNi+rKJqfH8nzzz/c2wFAuauKPW27iqookSy52HMuUjgvlLsSBi1wHZ594ew9A2n/9HMjG2Zm2MYheOG/2MXzhr/LX5vF7Dmc9rzPR16Gtpum3cywTJJOJzelh1h30TzQH3D0obRGlWcfj17ls2evKKaTbiFy72ic9ZOKbL/LOSVXeE2ag17etqmBe/Z10tE/RoXXYm19Gc++3kdvOM5YLMlZHeXQqSHG48ms47EdhqLKZ1ETcE0psO3e7KkzjfhdV8ZFj8lMj39dubtofh4kLyx3Z2blWV+1seBFiGTJpZZljPNiUgocVeA/A6MR0DPMeewYBtc4OGeYqcJwQuVWGDwEL/09JMOw5Yv5a7eYPZcbzpyCFdmHE0wwAj4Cg8M8+tTLnIy6aQ56ed+6IIFH99u9zeb0h9vO1iresqGedQ1lRXHSLfbcPxZnQ+OFZcwDbnNKPtk/zvIqD2/d2MBPnu/kZP845V6TNXVl7D3eT184xsBYnPF4iudODBBJpMg2M6flNAj5LGrK3FMK7Mk92TWZ2x6rdMdjT/Q4v2l1bVHsX8n5yftPDvHSuVcoN4qrKJEsudhzLlI450NFPQz3gHUYYlfl3tZxzP7fH8q9HYBy2MXz0Ivwh/8DiWHY8fWlsYpfKfEFYCyMMTZI2jv9MtjK6yE1FGb9YDevNW3kPduX0f/oE7SiLxnbnM0dO5r48qNHiuaku1Ty4HiCjU0Xiupyj8mmpkuL7FvW13Pv/lN09o9T5jVZXRvguRP99I/GOTMcYWAszpNHe4kmsn+A8loOQn4XtZmZRUKTZhWZPFwk6HNhOYtrPPZE4fzE0XOMxVJFtf8kzy/v2dLIfQdOMxgdYUORFSWSJRd7zkUK53ywyiDpBvcrMxfO58c3Tz938BTKgIotMPwyHP2GPWxj1w8W3mYxe/4yOHcax9AZ0tWN026mlMLh87BmdIz3t5fz0L7X+ctIH8rlwjDNGb+M13Ly7u1NjMaSvHt79inqJC9uHo4k2LTsQlFd6bXYctGY32VVHm5eW2fngXHKPSYrawI83zHAwGick/0puoci9I/Gp51ZpMztpDrgojbTk33JrCKZ/6t8Fo7LMB57YqjG2ZHYlOExhd4fkueff7q/E4CAVcGetpuLqiiRLLnYcy5SOOeDUmAGwdENIyOgp5tbWYOvB3Rgbr3GSkH5Rhj5I3TcDYlR2PH9vDRdzILTBMOBY+gMiRk2VQEf6dEx+vYeYl00iQNQFTP3Nk9YW1/GO7Y0FPykK3nmPBJNTimqgz6LbcunFtlNlR5uXFPL/Qe66BwYp8zjpK06wAsn7SJ7OJLg9d5R+kfj0y5CU+G1e67tRWjcWWcVqQ64KPeY8x6PncjMqtFQIWOcl0ruHIiggPbyNQUvQiRLLrUsY5wvh4oGGOy253SOXZt9G8cgWFEw6+f++kpB2VoIO6D7QYi9E2hbUJPFLCkFLheOcO/MmzocKJfFxki/XTS7rFn1Nk92VUsVDx48XVQnYckLy6OxJFsnFdUhv4vtzZVTtm+ocPOm1bU8cLCLUwMRAm4nrdU+DnYOcW4kSl84BmqEobE4qSzjsZ2GIuifKLLdlwwRmZx9lmNKkT2xAMqt6+uL4v2SnJ+8v2OAVweOUOMrrqJEsuRizznP8zrbFTFFaMcqn95/585CNyO37n2QUjDWnv1xcwDKOqHiKrBmOVQjm/AxiHTBSBOMvDczn/Qic3SCdXzxv04xibdDKjM0o7cHBs4xuvvP0O6LepAV4Lww+0I6Fkf39qMBozY0pXDWWkM8gdn1Cq4VjahpLhgcjiQYsALclV5Jx0C0KE7Ckosjd/SN0VDh4fpVNTx4qItTgxH8LictIR+HTg0xOJbAUKCB4fEE2X7Du50GQf/ESo8uRiJJnj3ez1s31jIwduk85JJLM7//28/i8fXTWhdlddVqXht4jeH4MOVWuWTJkqfJQa+f+z70n6f9850Uzvk0dApir9uF1HSSXqi/auEX+IVPwHAvnHivveqgy7O4Fw16/wMqp18EZEk60wLpO+zbsRjprg5iN30YHaiYsll6OEyqdwBHffX5+5Jn+1AOB45Q5ZRt9XgEwxjEs/dRjEg455dPOkyO/N13UZaF6TBIpOzlrg3FvHPffzwF1+3EMg06+sY4MxzFUIptzZU8cvgMR8+NFs1JX3J+iuy6cjdvbK/mFy+epitTZC8Pennp1DCDkTgGipTWJFNp1jeW0VbtL5r2S15Y/n8vnyEcTeXjt6EQVwzLCUf+4bZpCyoZqpFPFcuA3Fdj5o2vCTx1sPOv4bEHoP8cmObiFdB910OlA2qC+X/tYtSjoFvDxKgalwsjVIPnXe8Ax9RpxxIdpwjf/fMphbOz9tJZU3QqDYaBJ/gzjK89Ap/5CJRXTdsE5/AA65qrp318ProffoTGj9wGwNWtU/fltStDxJIpRiIJhsYTvGf7MsZiSdxOg2Nnw9QE3NSVedjZWsUDB7oKXhRInjm3hHzncyyZZkfLheEh9RVurmmrKqr2Ss5vfuvG+qJqj2TJpZB/d/LpnOdRKZxLmQLWbYM1W+DVF+Gx+xengNYaUinwJfPzeqVghum4J3Mua4BUGp1MoZw55vKNxbDWtWMMxhfevkXicjqoDjioDrin3H9N29QPAm9oCxJNpBmJxhkcT3D7libG40m8loPXesJU+ewp2XatCHK/FNmSJUuWLLlEsunLPQ2AFM5LgWHAuq2wZjO89hI8ej/0n7Vng3DnoYBOJKCiChylMawnL9wa9OzeN+VwkOgZxGxtBGf2ilun7d5mc91qyP1htiQopfBYDjyWh9qyqd/zrouK7F1tQUZjScLRJEPjcd6+qZFIPIXP7eAPp0co91hsX+5id3uI+16QIluyZMmSJRcuX1Wdu+dMCuelxDBg7RZYvSm/BXQiBivX5retxc7C7tHPFLwzSfUOg1JorbNPCRaNYa5ZieFxX/rYEqeUIuA2CbhNGiqm/kJ6w0VF9tWtQcLRBKPRJAPjcd6yoZ5IIkW5x+Rw9zA+y2TrskquX1VdNL9kJUuWLFny0smra3KXxlI4L0UTBfT5Huj7oG8BBXQqBctXQv/iNLcoKcCVgmQSLGvGzXUihbOpgWTXabioOJ7obbbWr16kxi4dDkNR4bWo8Fo0VU1dpfHiInt7SyXDkQRjsSQDYzHevK6OaCJFhcfixVNDuJ0ONjeVc+MaWUZasmTJkiXPLj/V+QKsmf48JYXzUqaUXTxP7oHu65l7Ae1wQm3jlVU4A3iTkEpgdz/PzFrbTrLr9KW9ztEY5uqVGN45DJwWMzIdBiG/PTdxc3Dq9I6726cW2ZuXVTAcSTAeTzEwGuemNbVEEymqfC4Odg7iNAw2NJTz5vVSZEuWLFnylZx9gdwz0UjhfCW4uIB+7H57XuLZFNBaQzoFNY1whc1GhzcJw7O/INKoCWL4faQjUbDsuZvt3maFtX7VYrVSzILbdOA27Qs3W0NTi+xrJxXZWmvWN5QzFEkQjafoG4txw6oaoskU1X4X+zsGMZRiXX0Zt26QxUIkS5YseanlXTWunOcTKZyvJJcU0A9A7xlwOsHtzV5Ap1L2Y/7ZLxu9ZHiTMJSe9eZKKcz1q4k9dwDILHoSi2O2t2L4vDmfK4qDUgqfy4nPZf9qbMM/5fFr2y9MD6i1ZnVdgOHxBLFkir7RGNeurOaqljR1ZS72negnnYbVdQHetkmWUZcsWbLkUsjt1bnP11I4X4kmF9BHXraHcExXQCdi9vjmK5E7ZQ9TmQOzpYnY84fsOZszb6O1IcdgKVGylFKUe0zKPfaHpPbaqR8uJxfZyVSaldV+hiMJookUvaMxdq0IsX15ivpyD8++3k8iqWmvCfCOLVJkS5YsWXKh8t5Tr1z+Mc5KKQO4E9gMxICPACuBLwCdwJ9qrdNKqX8F/llr3bEY7RAzUMounldthCOH4bH74NxFBXQiDsvbCt3SwnCn5nwhpTJNzPZWEq8eA8Apvc0CcDoMKn0WlT57vPzFv5PfuOpCkR1PpmkJ+hiJxIkn05wbjbGzJciWZZU0VXh4+lgfkXiatmo/t29tLPhJRrJkyZKXUq4sy/2X5sXqcb4dcGutdymlrgH+Bbv/7c3A54HNSqkkMCJFcxFQClZvhFUbMgX0/XDutF1AO5xQv7zQLSwMd8oeqjJH1uo2u3BWCpf0Nos5spwG1QEX1QF7nN3FE0FOLrKjiRTLKr0MR+Mkkppz4Sjbl1exqamc5ZU+njzay2gsxYqQn3dukyJbsmTJkmfMtbnHOCut87+ohVLqy8A+rfU9mdwN/B74JHbh/CXgc8Bfaq2HZ/OaO1a69f6vXmFzCeei02CVw81PLMJra7uA/vX9cLYb/vYfoaoaHl4L7rr8f71iFemBF2+GVOYCwXQaPvv1S5bcBui+/W9o/PlXz+fxR3+P8vvwvGHHpa/76zfBn/xuxiW3GR6Af/jOQr+LnO0UVwatNWPxFEPjcUYiCVJpzdmRGOfCMZLpNK1BH/s6BugbjRHyu9jZWsW+E5IlS5Z85eWtLQ5uXLVy2j83L1bh/B3gPq31LzO5E7gN+J/AAeAg0AqkgC3A97XWz2Z5nY8CH83EDcDhvDdWFEII6Ct0I0ReyL5cWmR/Lh2yL5cO2ZeXX5/W+tZsDyxmj/NerfW9mdyltW7K3HYA92KPe/4ucAfwkNb6rTO85n6tdZbuO1FqZF8uHbIvlxbZn0uH7MulQ/ZlcZl5LeH5eRp4K0BmjPPLkx77KPC9SV9fA1MnVhVCCCGEEKLILNbFgQ8ANyulnsG+KPDPAZRSZcANWuv3ZnIPdpF95yK1QwghhBBCiLxYlMJZa50GPp7l/hHgvZPyx+bwst/KQ9NEcZB9uXTIvlxaZH8uHbIvlw7Zl0VkUcY4CyGEEEIIsdQs1hhnIYQQQgghlpSiLpyVUoZS6ptKqWeVUo8rpa7QtZ+XDqXUwcy+fFwp9X8L3R4xd0qpq5VSj2dur1RKPaWUelIp9Y3MqqGiRFy0L7cppbonHZ/vneHpokgopUyl1A8yx+E+pdQ75NgsTdPsSzk2i8hiXRyYL9lWINxT4DaJeVJKuQG01jcUuClinpRSnwI+CIxl7voy8Bmt9eNKqW9iH58PFKp9Yvay7MttwJe11v9SuFaJefoA0K+1/qBSKoi9VsIh5NgsRdn25ReQY7NoFPsn0GuBXwForfcCMo9hadsMeJVSjyqlfpv5MCRKy+vAuybl7dirggL8EviTy94iMV/Z9uVtSqknlFL/ppQKFKhdYu5+CvyPSTmJHJularp9KcdmkSj2wrkMmLwkd0opVey95GJ648A/A7dgz7pyt+zP0qK1vg9ITLpL6QtXGIeB8svfKjEfWfblPuCTWuvrgOPA5wrSMDFnWutRrXU4U1D9DPgMcmyWpGn2pRybRaTYC+cRYPInK0NrnSxUY8SCHQF+qG1HgH6gvsBtEguTnnQ7AAwVqiFiwR7QWr8wcRvYWsjGiLlRSi0Dfgf8QGv9I+TYLFlZ9qUcm0Wk2AvnXCsQitLzn7DHqaOUasD+i8KZgrZILNRBpdQNmdtvAZ4sYFvEwjyilNqZuX0T8EKujUXxUErVAo8Cn9ZafzdztxybJWiafSnHZhEp6nmcM1cB3wlsIrMCodb61cK2SsyXUsrCXm59OfZS65/WWj9T0EaJOVNKtQD3aK2vUUqtAr4NWMAfgb/QWqcK2DwxBxfty23AvwJxoAf4aGbRKlHklFJfwV5cbPL58RPAV5Fjs6RMsy//Hvgn5NgsCkVdOAshhBBCCFEsin2ohhBCCCGEEEVBCmchhBBCCCFmQQpnIYQQQgghZkEKZyGEEEIIIWZBCmchhBBCCCFmQQpnIYQoYUopt1Kqo9DtEEKIK4EUzkIIIYQQQsyCs9ANEEIIMTdKKT9wN1AJHMvcdz3wucwmXuBDwA1Au9b6k0opB3AIuBb4AVAOeIBPaa0fv5ztF0KIUiU9zkIIUXo+DBzWWl8H3JW5bz3wAa31jcBDwB3Aj4HbM0XzrcDvgCagDng78H7sIlsIIcQsSI+zEEKUnvXArwC01s8ppRJAN/BVpdQo0Ag8rbUOK6V+D9wC/DnwBa31K0qpr2MX1Sb2ssxCCCFmQQpnIYQoPa8Cu4AHlVJbsQvg7wArMsXy9wGV2fbbwKeBkNb6JaXURiCgtb5NKVUPPAM8fPm/BSGEKD0yVEMIIUrP14FGpdRTwF8BMexxy88ppZ4GAkAD2D3SwErsMdEAR4EblFL7gJ8Cn73MbRdCiJKltNaFboMQQohFopQygKeBW7TWI4VujxBClDLpcRZCiCVKKdUKHAD+XYpmIYRYOOlxFkIIIYQQYhakx1kIIYQQQohZkMJZCCGEEEKIWZDCWQghhBBCiFmQwlkIIYQQQohZkMJZCCGEEEKIWZDCWQghhBBCiFn4/ycmCf9t4M3rAAAAAElFTkSuQmCC\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = model.figure_infections(combine_Q_infected=False, plot_Q_R='stacked', plot_Q_S='stacked')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file

From c974004e161a2c50dfc610b4c4ac3d0e5d996276 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 14:00:03 -0400
Subject: [PATCH 07/19] Add support for logging history, as well as as policy
 to stop simulation if a certain condition is met, and ability to supress
 print statements.

---
 seirsplus/sim_loops.py | 77 ++++++++++++++++++++++++++++++++++++------
 1 file changed, 66 insertions(+), 11 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index ae9006d..5131cea 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -18,10 +18,16 @@ def run_tti_sim(model, T,
                 isolation_compliance_positive_contact=[None], isolation_compliance_positive_contactgroupmate=[None],
                 isolation_lag_symptomatic=1, isolation_lag_positive=1, isolation_lag_contact=0, isolation_groups=None,
                 cadence_testing_days=None, cadence_cycle_length=28, temporal_falseneg_rates=None,
-                test_priority = 'random'
+                test_priority = 'random',
                 # test_priority: how to to choose which nodes to test:
                 # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
                 # A suffix of "degree_oblivious" means that we ignore degrees (i.e., assume we don't know social networks for testing policy)
+                history = None,
+                # history is a  dictonary that, if provided, will be updated with history and summary information for logging
+                stopping_policy=None,
+                # stopping_policy: function that takes as input the model  and decides whether to stop execution
+                # it also takes as a additional two inputs the history and summary  to record data on why execution was stopped
+                verbose = True, # suppress printing if verbose is false - useful for running many simulations in parallel
                 ):
 
     #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -79,10 +85,44 @@ def run_tti_sim(model, T,
 
     model.tmax  = T
     running     = True
+
+
+    def log(d):
+        # log values in dictionary d into history dict
+        nonlocal history
+        nonlocal model
+        if not history: return
+        if model.t in history:
+            history[model.t].update(d)
+        else:
+            history[model.t] = dict(d)
+
+    def vprint(s):
+        # print s if verbose is true
+        if verbose: print(s)
+
+
     while running:
 
         running = model.run_iteration()
 
+        if history: # log current state of the model
+            d = {}
+            for att in ["numS","numE","numI","numR","numF","numQ_E","numQ_I"]:
+                d[att] = getattr(model,att)[model.tidx]
+                if (model.nodeGroupData):
+                    for groupName, groupData  in enumerate(model.nodeGroupData):
+                        d[groupName+"/"+att] = groupData[att][model.tidx]
+            log(d)
+
+
+        if running and stopping_policy:
+            running = stopping_policy(model)
+            if not running:
+                self.finalize_data_series()
+
+
+
         #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         # Introduce exogenous exposures randomly:
         #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -93,9 +133,10 @@ def run_tti_sim(model, T,
             numNewExposures = numpy.random.poisson(lam=average_introductions_per_day)
             
             model.introduce_exposures(num_new_exposures=numNewExposures)
+            log({"numNewExposures": numNewExposures})
 
             if(numNewExposures > 0):
-                print("[NEW EXPOSURE @ t = %.2f (%d exposed)]" % (model.t, numNewExposures))
+                vprint("[NEW EXPOSURE @ t = %.2f (%d exposed)]" % (model.t, numNewExposures))
 
         #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
         # Execute testing policy at designated intervals:
@@ -109,6 +150,7 @@ def run_tti_sim(model, T,
 
             currentNumInfected = model.total_num_infected()[model.tidx]
             currentPctInfected = model.total_num_infected()[model.tidx]/model.numNodes
+            log({"currentNumInfected": currentNumInfected})
 
             if(currentPctInfected >= intervention_start_pct_infected and not interventionOn):
                 interventionOn        = True
@@ -116,7 +158,7 @@ def run_tti_sim(model, T,
             
             if(interventionOn):
 
-                print("[INTERVENTIONS @ t = %.2f (%d (%.2f%%) infected)]" % (model.t, currentNumInfected, currentPctInfected*100))
+                vprint("[INTERVENTIONS @ t = %.2f (%d (%.2f%%) infected)]" % (model.t, currentNumInfected, currentPctInfected*100))
                 
                 nodeStates                       = model.X.flatten()
                 nodeTestedStatuses               = model.tested.flatten()
@@ -384,14 +426,14 @@ def run_tti_sim(model, T,
                 tracingPoolQueue.append(newTracingPool)
 
 
-                print("\t"+str(numTested_symptomatic) +"\ttested due to symptoms  [+ "+str(numPositive_symptomatic)+" positive (%.2f %%) +]" % (numPositive_symptomatic/numTested_symptomatic*100 if numTested_symptomatic>0 else 0))
-                print("\t"+str(numTested_tracing)     +"\ttested as traces        [+ "+str(numPositive_tracing)+" positive (%.2f %%) +]" % (numPositive_tracing/numTested_tracing*100 if numTested_tracing>0 else 0))            
-                print("\t"+str(numTested_random)      +"\ttested randomly         [+ "+str(numPositive_random)+" positive (%.2f %%) +]" % (numPositive_random/numTested_random*100 if numTested_random>0 else 0))            
-                print("\t"+str(numTested)             +"\ttested TOTAL            [+ "+str(numPositive)+" positive (%.2f %%) +]" % (numPositive/numTested*100 if numTested>0 else 0))           
+                vprint("\t"+str(numTested_symptomatic) +"\ttested due to symptoms  [+ "+str(numPositive_symptomatic)+" positive (%.2f %%) +]" % (numPositive_symptomatic/numTested_symptomatic*100 if numTested_symptomatic>0 else 0))
+                vprint("\t"+str(numTested_tracing)     +"\ttested as traces        [+ "+str(numPositive_tracing)+" positive (%.2f %%) +]" % (numPositive_tracing/numTested_tracing*100 if numTested_tracing>0 else 0))
+                vprint("\t"+str(numTested_random)      +"\ttested randomly         [+ "+str(numPositive_random)+" positive (%.2f %%) +]" % (numPositive_random/numTested_random*100 if numTested_random>0 else 0))
+                vprint("\t"+str(numTested)             +"\ttested TOTAL            [+ "+str(numPositive)+" positive (%.2f %%) +]" % (numPositive/numTested*100 if numTested>0 else 0))
 
-                print("\t"+str(numSelfIsolated_symptoms)        +" will isolate due to symptoms         ("+str(numSelfIsolated_symptomaticGroupmate)+" as groupmates of symptomatic)")
-                print("\t"+str(numPositive)                     +" will isolate due to positive test    ("+str(numIsolated_positiveGroupmate)+" as groupmates of positive)")
-                print("\t"+str(numSelfIsolated_positiveContact) +" will isolate due to positive contact ("+str(numSelfIsolated_positiveContactGroupmate)+" as groupmates of contact)")
+                vprint("\t"+str(numSelfIsolated_symptoms)        +" will isolate due to symptoms         ("+str(numSelfIsolated_symptomaticGroupmate)+" as groupmates of symptomatic)")
+                vprint("\t"+str(numPositive)                     +" will isolate due to positive test    ("+str(numIsolated_positiveGroupmate)+" as groupmates of positive)")
+                vprint("\t"+str(numSelfIsolated_positiveContact) +" will isolate due to positive contact ("+str(numSelfIsolated_positiveContactGroupmate)+" as groupmates of contact)")
 
                 #----------------------------------------
                 # Update the status of nodes who are to be isolated:
@@ -414,7 +456,20 @@ def run_tti_sim(model, T,
                     model.set_isolation(isolationNode, True)
                     numIsolated += 1
 
-                print("\t"+str(numIsolated)+" entered isolation")
+                vprint("\t"+str(numIsolated)+" entered isolation")
+                log({"numTested_symptomatic": numTested_symptomatic,
+                     "numPositive_symptomatic" : numPositive_symptomatic,
+                     "numTested_tracing" : numTested_tracing,
+                     "numPositive_tracing" : numPositive_tracing,
+                     "numTested" : numTested,
+                     "numSelfIsolated_symptoms":  numSelfIsolated_symptoms,
+                    "numSelfIsolated_symptomaticGroupmate": numSelfIsolated_symptomaticGroupmate
+                    "numPositive" : numPositive,
+                     "numIsolated_positiveGroupmate" : numIsolated_positiveGroupmate,
+                     "numSelfIsolated_positiveContact" : numSelfIsolated_positiveContact,
+                     "numIsolated" : numIsolated
+                    })
+
                 
             #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

From 309f7890adba715869bf277254bf33fab1f64906 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 14:08:57 -0400
Subject: [PATCH 08/19] Realized that "obvious testing" can already be realized
 using the `random_testing_degree_bias` parameter.

---
 seirsplus/sim_loops.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 5131cea..d986621 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -21,7 +21,6 @@ def run_tti_sim(model, T,
                 test_priority = 'random',
                 # test_priority: how to to choose which nodes to test:
                 # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
-                # A suffix of "degree_oblivious" means that we ignore degrees (i.e., assume we don't know social networks for testing policy)
                 history = None,
                 # history is a  dictonary that, if provided, will be updated with history and summary information for logging
                 stopping_policy=None,
@@ -301,10 +300,7 @@ def vprint(s):
                         numRandomTests = max(min(tests_per_day-len(tracingSelection)-len(symptomaticSelection), len(testingPool)), 0)
                         
                         testingPool_degrees       = model.degree.flatten()[testingPool]
-                        if "degree_oblivious" in test_priority:
-                            testingPool_degreeWeights = numpy.ones(len(testingPool))
-                        else:
-                            testingPool_degreeWeights = numpy.power(testingPool_degrees,random_testing_degree_bias)/numpy.sum(numpy.power(testingPool_degrees,random_testing_degree_bias))
+                        testingPool_degreeWeights = numpy.power(testingPool_degrees,random_testing_degree_bias)/numpy.sum(numpy.power(testingPool_degrees,random_testing_degree_bias))
 
                         poolSize = len(testingPool)
                         if(poolSize > 0):

From b686d5f1fcd3e1dda045dac3a336be902bc32cb0 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 19:11:31 -0400
Subject: [PATCH 09/19] fix bug

---
 seirsplus/sim_loops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index d986621..6ceb9e2 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -459,7 +459,7 @@ def vprint(s):
                      "numPositive_tracing" : numPositive_tracing,
                      "numTested" : numTested,
                      "numSelfIsolated_symptoms":  numSelfIsolated_symptoms,
-                    "numSelfIsolated_symptomaticGroupmate": numSelfIsolated_symptomaticGroupmate
+                    "numSelfIsolated_symptomaticGroupmate": numSelfIsolated_symptomaticGroupmate,
                     "numPositive" : numPositive,
                      "numIsolated_positiveGroupmate" : numIsolated_positiveGroupmate,
                      "numSelfIsolated_positiveContact" : numSelfIsolated_positiveContact,

From 581e48c9dc451ad8511060f79db2a16f9e913911 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 19:33:00 -0400
Subject: [PATCH 10/19] adding correct attributes, giving stopping policy
 access to history

---
 seirsplus/sim_loops.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 6ceb9e2..b7480a0 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -24,8 +24,7 @@ def run_tti_sim(model, T,
                 history = None,
                 # history is a  dictonary that, if provided, will be updated with history and summary information for logging
                 stopping_policy=None,
-                # stopping_policy: function that takes as input the model  and decides whether to stop execution
-                # it also takes as a additional two inputs the history and summary  to record data on why execution was stopped
+                # stopping_policy: function that takes as input the model  and current history and decides whether to stop execution
                 verbose = True, # suppress printing if verbose is false - useful for running many simulations in parallel
                 ):
 
@@ -107,7 +106,10 @@ def vprint(s):
 
         if history: # log current state of the model
             d = {}
-            for att in ["numS","numE","numI","numR","numF","numQ_E","numQ_I"]:
+
+
+        statistics = ["numS","numE","numI_pre","numI_sym","numI_asym","numH","numR","numF","numQ_S","numQ_E","numQ_pre","numQ_sym","numQ_asym","numQ_R"]
+        for att in statistics:
                 d[att] = getattr(model,att)[model.tidx]
                 if (model.nodeGroupData):
                     for groupName, groupData  in enumerate(model.nodeGroupData):
@@ -116,7 +118,7 @@ def vprint(s):
 
 
         if running and stopping_policy:
-            running = stopping_policy(model)
+            running = stopping_policy(model,history)
             if not running:
                 self.finalize_data_series()
 

From 50b4f515fda47ded203aa2eb6a626bf2c5bd36f1 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 19:36:06 -0400
Subject: [PATCH 11/19] stopping policy returns True to stop

---
 seirsplus/sim_loops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index b7480a0..68d8d6c 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -25,6 +25,7 @@ def run_tti_sim(model, T,
                 # history is a  dictonary that, if provided, will be updated with history and summary information for logging
                 stopping_policy=None,
                 # stopping_policy: function that takes as input the model  and current history and decides whether to stop execution
+                #                  returns True to stop, False to continue running
                 verbose = True, # suppress printing if verbose is false - useful for running many simulations in parallel
                 ):
 
@@ -118,7 +119,7 @@ def vprint(s):
 
 
         if running and stopping_policy:
-            running = stopping_policy(model,history)
+            running = not stopping_policy(model,history)
             if not running:
                 self.finalize_data_series()
 

From f7823ae64cf92e6f2efbfb8c0538c5ea4b5a0d4a Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 19:42:49 -0400
Subject: [PATCH 12/19] fix indent bug

---
 seirsplus/sim_loops.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 68d8d6c..0ce44fe 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -23,6 +23,7 @@ def run_tti_sim(model, T,
                 # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
                 history = None,
                 # history is a  dictonary that, if provided, will be updated with history and summary information for logging
+                # OrderedDict is optional but may be better for efficiency in some stopping policies
                 stopping_policy=None,
                 # stopping_policy: function that takes as input the model  and current history and decides whether to stop execution
                 #                  returns True to stop, False to continue running
@@ -115,7 +116,7 @@ def vprint(s):
                 if (model.nodeGroupData):
                     for groupName, groupData  in enumerate(model.nodeGroupData):
                         d[groupName+"/"+att] = groupData[att][model.tidx]
-            log(d)
+        log(d)
 
 
         if running and stopping_policy:

From d4b60d78a96482c0fe9b097ebde3bc7c46c6cc27 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 19:54:57 -0400
Subject: [PATCH 13/19] fix bug

---
 seirsplus/sim_loops.py | 25 ++++++++++++-------------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 0ce44fe..7ba7e3b 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -89,8 +89,8 @@ def run_tti_sim(model, T,
 
     def log(d):
         # log values in dictionary d into history dict
-        nonlocal history
-        nonlocal model
+        #nonlocal history # uncomment for Python 3.x
+        #nonlocal model   # uncomment for Python 3.x
         if not history: return
         if model.t in history:
             history[model.t].update(d)
@@ -106,23 +106,22 @@ def vprint(s):
 
         running = model.run_iteration()
 
-        if history: # log current state of the model
+        if not (history is None): # log current state of the model
             d = {}
-
-
-        statistics = ["numS","numE","numI_pre","numI_sym","numI_asym","numH","numR","numF","numQ_S","numQ_E","numQ_pre","numQ_sym","numQ_asym","numQ_R"]
-        for att in statistics:
-                d[att] = getattr(model,att)[model.tidx]
-                if (model.nodeGroupData):
-                    for groupName, groupData  in enumerate(model.nodeGroupData):
-                        d[groupName+"/"+att] = groupData[att][model.tidx]
-        log(d)
+            statistics = ["numS","numE","numI_pre","numI_sym","numI_asym","numH","numR","numF","numQ_S","numQ_E","numQ_pre","numQ_sym","numQ_asym","numQ_R"]
+            for att in statistics:
+                    d[att] = getattr(model,att)[model.tidx]
+                    if (model.nodeGroupData):
+                        for groupName  in model.nodeGroupData:
+                            groupData = model.nodeGroupData[groupName]
+                            d[groupName+"/"+att] = groupData[att][model.tidx]
+            log(d)
 
 
         if running and stopping_policy:
             running = not stopping_policy(model,history)
             if not running:
-                self.finalize_data_series()
+                model.finalize_data_series()
 
 
 

From b38d2444779b9f3043730f44575f97804a2c4db1 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 20:04:12 -0400
Subject: [PATCH 14/19] fix bug

---
 seirsplus/sim_loops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 7ba7e3b..7de796e 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -91,7 +91,7 @@ def log(d):
         # log values in dictionary d into history dict
         #nonlocal history # uncomment for Python 3.x
         #nonlocal model   # uncomment for Python 3.x
-        if not history: return
+        if history is None: return #o/w assume it's a dictionary
         if model.t in history:
             history[model.t].update(d)
         else:

From 7411f1b38d31521e068347276915881def1aca3f Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 20:22:37 -0400
Subject: [PATCH 15/19] wip

---
 seirsplus/__pycache__/FARZ.cpython-37.pyc      | Bin 0 -> 19005 bytes
 seirsplus/__pycache__/__init__.cpython-37.pyc  | Bin 0 -> 164 bytes
 seirsplus/__pycache__/models.cpython-37.pyc    | Bin 0 -> 93587 bytes
 seirsplus/__pycache__/networks.cpython-37.pyc  | Bin 0 -> 17654 bytes
 seirsplus/__pycache__/sim_loops.cpython-37.pyc | Bin 0 -> 10039 bytes
 seirsplus/__pycache__/utilities.cpython-37.pyc | Bin 0 -> 2947 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 seirsplus/__pycache__/FARZ.cpython-37.pyc
 create mode 100644 seirsplus/__pycache__/__init__.cpython-37.pyc
 create mode 100644 seirsplus/__pycache__/models.cpython-37.pyc
 create mode 100644 seirsplus/__pycache__/networks.cpython-37.pyc
 create mode 100644 seirsplus/__pycache__/sim_loops.cpython-37.pyc
 create mode 100644 seirsplus/__pycache__/utilities.cpython-37.pyc

diff --git a/seirsplus/__pycache__/FARZ.cpython-37.pyc b/seirsplus/__pycache__/FARZ.cpython-37.pyc
new file mode 100644
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From 977b9e5c15e6688c8091e4a2946bf3334cb5adc2 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Wed, 19 Aug 2020 20:25:21 -0400
Subject: [PATCH 16/19] ignore temp files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 76386e1..a856bd4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,4 @@
 *.swp
 
 .DS_Store
+/seirsplus/.ipynb_checkpoints/

From ae6d27036d898d66faed9a22360b4bfa87b12c43 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Thu, 20 Aug 2020 17:59:30 -0400
Subject: [PATCH 17/19] Added ability to introduce different number of
 exposures per group, and allow the priority of testing to be an arbitrary
 function

---
 seirsplus/models.py    | 32 ++++++++++++++++++++++++++------
 seirsplus/sim_loops.py | 12 ++++++++++--
 2 files changed, 36 insertions(+), 8 deletions(-)

diff --git a/seirsplus/models.py b/seirsplus/models.py
index 127abac..4a408f2 100644
--- a/seirsplus/models.py
+++ b/seirsplus/models.py
@@ -1934,6 +1934,21 @@ def __init__(self, G, beta, sigma, lamda, gamma,
          
 #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    # Two helper functions to get the node list and mask for a group
+    # If groupName is `all` then returns this for all vertices,
+    # even if nodeGroups were not set.
+    def get_nodes(self,groupName='all'):
+        if groupName=='all':
+            return range(self.numNodes)
+        return self.nodeGroupData[groupName]['nodes']
+
+    def get_mask(self,groupName='all'):
+        if groupName=='all':
+            return numpy.ones(shape=(self.numNodes,1))
+        return self.nodeGroupData[groupName]['mask']
+
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     def update_parameters(self):
 
@@ -2483,12 +2498,17 @@ def set_positive(self, node, positive):
 #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     def introduce_exposures(self, num_new_exposures):
-        exposedNodes = numpy.random.choice(range(self.numNodes), size=num_new_exposures, replace=False)
-        for exposedNode in exposedNodes:
-            if(self.X[exposedNode]==self.S):
-                self.X[exposedNode] = self.E
-            elif(self.X[exposedNode]==self.Q_S):
-                self.X[exposedNode] = self.Q_E
+        # If num_new_exposure is dictionary of the form {"group_1": num_1, "group_2": num_2 , ... }
+        # then introduce num_i exposures to group_i
+        if not isinstance(num_new_exposures,dict):
+            num_new_exposures = {"all": num_new_exposures }
+        for group,num in num_new_exposures.items():
+            exposedNodes = numpy.random.choice(self.get_nodes(group), size=num, replace=False)
+            for exposedNode in exposedNodes:
+                if(self.X[exposedNode]==self.S):
+                    self.X[exposedNode] = self.E
+                elif(self.X[exposedNode]==self.Q_S):
+                    self.X[exposedNode] = self.Q_E
 
 
 #^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 7de796e..0070634 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -21,6 +21,7 @@ def run_tti_sim(model, T,
                 test_priority = 'random',
                 # test_priority: how to to choose which nodes to test:
                 # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
+                # if test_priority is callable then use as a key to sort nodes (lower value is higher priority)
                 history = None,
                 # history is a  dictonary that, if provided, will be updated with history and summary information for logging
                 # OrderedDict is optional but may be better for efficiency in some stopping policies
@@ -132,7 +133,12 @@ def vprint(s):
 
             timeOfLastIntroduction = model.t
 
-            numNewExposures = numpy.random.poisson(lam=average_introductions_per_day)
+            if isinstance(average_introductions_per_day,dict):
+                numNewExposures = {}
+                for group,num in average_introductions_per_day.items():
+                    numNewExposures[group] = numpy.random.poisson(lam=num)
+            else:
+                numNewExposures = numpy.random.poisson(lam=average_introductions_per_day)
             
             model.introduce_exposures(num_new_exposures=numNewExposures)
             log({"numNewExposures": numNewExposures})
@@ -307,7 +313,9 @@ def vprint(s):
 
                         poolSize = len(testingPool)
                         if(poolSize > 0):
-                            if 'last_tested' in test_priority:
+                            if callable(test_priority):
+                                randomSelection = sorted(testingPool, key=test_priority)[:numRandomTests]
+                            elif test_priority == 'last_tested':
                                 # sort the pool according to the time they were last tested, breaking ties randomly
                                 randomSelection = sorted(testingPool,key = lambda i: (model.testedTime[i], random.randint(0,poolSize*poolSize)))[:numRandomTests]
                             else:

From 5921093c5587c2f51c0a9b6084ee38f64c98a3e5 Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Mon, 24 Aug 2020 15:48:03 -0400
Subject: [PATCH 18/19] comment typos

---
 seirsplus/sim_loops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/seirsplus/sim_loops.py b/seirsplus/sim_loops.py
index 0070634..c90cd2f 100644
--- a/seirsplus/sim_loops.py
+++ b/seirsplus/sim_loops.py
@@ -23,8 +23,8 @@ def run_tti_sim(model, T,
                 # 'random' - use test budget for random fraction of eligible population, 'last_tested' - sort according to the time passed since testing (breaking ties randomly)
                 # if test_priority is callable then use as a key to sort nodes (lower value is higher priority)
                 history = None,
-                # history is a  dictonary that, if provided, will be updated with history and summary information for logging
-                # OrderedDict is optional but may be better for efficiency in some stopping policies
+                # history is a  dictionary that, if provided, will be updated with history and summary information for logging
+                # it preferably should be OrderedDict if we want to preserve ordering of logs
                 stopping_policy=None,
                 # stopping_policy: function that takes as input the model  and current history and decides whether to stop execution
                 #                  returns True to stop, False to continue running

From fb69f6e7e3dd855e2dbc79ab0493e32048a3156b Mon Sep 17 00:00:00 2001
From: Boaz Barak <b@boazbarak.org>
Date: Mon, 24 Aug 2020 15:56:16 -0400
Subject: [PATCH 19/19] Added functions to map dictionary of history into
 pandas DataFrame and Series of the summary

---
 seirsplus/utilities.py | 38 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/seirsplus/utilities.py b/seirsplus/utilities.py
index b8e9720..f16acc1 100644
--- a/seirsplus/utilities.py
+++ b/seirsplus/utilities.py
@@ -1,3 +1,4 @@
+import sys
 import numpy
 import matplotlib.pyplot as pyplot
 
@@ -61,6 +62,43 @@ def results_summary(model):
     print("peak  pct hospitalized: %0.2f%%" % (numpy.max(model.numH)/model.numNodes * 100) )
 
 
+#########################################################################################################################################
+# Logging packages - requires pandas
+
+try:
+    import pandas as pd
+
+    def last(x):
+        """Return last element of a pandas Series"""
+        return x.iloc[-1]
+
+
+    def hist2df(history):
+        """Take history dictionary and return:
+        pandas DataFrame of all history
+        pandas Series of the summary of history, taking the last value and the sum, as well average over time (sum of scaled)"""
+        L = [{'time': t, **d} for t, d in history.items()]
+        tmax = L[-1]['time']
+        n = len(L)
+        df = pd.DataFrame(L)
+        df['interval_length'] = (df['time'] - df['time'].shift(1)).fillna(0)
+        temp = df.copy().fillna(0)
+        for col in df.columns:
+            if col == 'time': continue
+            temp[col + "/scaled"] = temp[col] * temp['interval_length'] / tmax
+        summary = temp.agg([last, numpy.sum])
+        summary = summary.stack()
+        summary.index = ['/'.join(reversed(col)).strip() for col in summary.index.values]
+        return df, summary
+
+except ImportError:
+    print("Warning: pandas missing - some logging functions will not work", file=sys.stderr)
+    def last(x):
+        raise NotImplementedError("This function requires pandas to work")
+
+    def hist2df(history):
+        raise NotImplementedError("This function requires pandas to work")
+