Table of Contents

1. Project Description
2. Installation Instructions
3. Usage Guide
4. Parameter Explanations
5. Example Outputs

Project Description

"Simulation" is a project that measures the effect of behavior changes on the spread of COVID-19. It answers questions such as how going out less, socially distancing from others, self-isolating when sick, and reducing contact with others in general affects the spread of the COVID-19. The implementation consists of a stochastic SIR based model with behavioral component parameters.

Installation Instructions

To install dependencies for this program do the following:

$ python -m venv .venv
$ source .venv/bin/activate
$ pip install -r requirements

Usage Guide

python simulation.py [-h] [-seed SEED] [-N_S0 N_S0] [-N_P0 N_P0] [-N_A0 N_A0] [-N_Y0 N_Y0] [-N_R0 N_R0] [-s S] [-p P] [-a A] [-y Y] [-cycles CYCLES] [-avg_steps AVG_STEPS] plot_name metrics_name N_S_name N_P_name N_A_name N_Y_name N_R_name time_name

Parameter Explanations

positional arguments:
  plot_name             plot file name to output 
  metrics_name          metrics csv file name to output
  N_S_name              Susceptible array csv file name to output
  N_P_name              Pre-symptomatic array csv file name to output
  N_A_name              Asymptomatic array csv file name to output
  N_Y_name              Symptomatic array csv file name to output
  N_R_name              Recovered array csv file name to output
  time_name             Time array csv file name to output

options:
  -h, –help            show this help message and exit
  -seed SEED            value used to initialize random number generator
  -N_S0 N_S0            initial amount of susceptible population
  -N_P0 N_P0            initial amount of pre-symptomatic population
  -N_A0 N_A0            initial amount of asymptomatic population
  -N_Y0 N_Y0            initial amount of symptomatic population
  -N_R0 N_R0            initial amount of recoverd population
  -s S                  the fraction of contact that susceptible members will reduce
  -p P                  the fraction of contact that pre-symptomatic members will reduce
  -a A                  the fraction of contact that asymptomatic members will reduce
  -y Y                  the fraction of contact that symptomatic members will reduce
  -cycles CYCLES        the number of cycles the simulation will run
  -avg_steps AVG_STEPS  the number of equally distant in time averages we will be computing over the simulation time

Example Outputs

Ensure that you have followed the installation instructions beforehand:
Note: Outputs can be slightly different due to stochastic properties.
$ python simulation.py plot.png metrics.csv N_S.csv N_P.csv N_A.csv N_Y.csv N_R.csv time_name.csv
$ cat ./metrics.csv

0	1	2	3	4	5
name	mean	std	min	max	conf
peak_infections	1607.7	54.329826062670215	1512.0	1721.0	(np.float64(1592.1028752724171), np.float64(1623.297124727583))
peak_times	95548.3358400622	9304.133210121096	75886.2527655324	121690.99321655945	(np.float64(92877.28502548918), np.float64(98219.38665463521))
attack_rates	0.908452	0.006205521412419749	0.8948	0.9214	(np.float64(0.9066705054051609), np.float64(0.9102334945948392))

Plot of default run

$ python simulation.py skeptic_plot.png skeptic_metrics.csv skeptic_N_S.csv skeptic_N_P.csv skeptic_N_A.csv skeptic_N_Y.csv skeptic_N_R.csv ignorance_time_name.csv -s 0.80 -p 0.80 -a 0.80 -y 0.30
$ cat ./skeptic_metrics.csv

0	1	2	3	4	5
name	mean	std	min	max	conf
peak_infections	618.1	56.63717860204549	487.0	752.0	(np.float64(601.8404754829234), np.float64(634.3595245170767))
peak_times	183043.46592964712	26366.99888389911	141785.27822713408	254357.7722571604	(np.float64(175473.9704961474), np.float64(190612.96136314684))
attack_rates	0.641204	0.02227233225326885	0.5922	0.6768	(np.float64(0.6348100103758165), np.float64(0.6475979896241835))

Plot of skeptic run

$ python simulation.py infecpop_plot.png infecpop_metrics.csv infecpop_N_S.csv infecpop_N_P.csv infecpop_N_A.csv infecpop_N_Y.csv infecpop_N_R.csv infecpop_time_name.csv -N_Y0 500
$ cat ./infecpop_metrics.csv

0	1	2	3	4	5
name	mean	std	min	max	conf
peak_infections	1889.76	52.01098345542026	1788.0	1993.0	(np.float64(1874.828573655607), np.float64(1904.691426344393))
peak_times	39536.204943631106	2214.2513855250227	35217.0101841283	44553.39182111935	(np.float64(38900.53283604108), np.float64(40171.877051221134))
attack_rates	0.9083520000000002	0.006593155238578862	0.8948	0.9204	(np.float64(0.9064592226425721), np.float64(0.9102447773574283))

Plot of infecpop run