Mesa SIR is an extension for Python's Agent Based Modeling Library Mesa. Mesa SIR provides the basic building blocks for an Agent Based Susceptible-Infected-Recovered (SIR) Epidemic model. The hope is others will improve upon it to make it a robust ABM extension to aid in understanding and decision making for both COVID-19 and future pandemics.
A full model version is available COVID MESA ABM
Please contribute to help make this extension better. Some ways to contribute are:
- Add the ability to bring in GIS extensions with Mesa Geo
- Make more complex populations
- Make more complex social dynamics
- Create Mesa MultiGrid option
- Add BatchRunner option
Mesa SIR requires
Mesa>=0.8.6
numpy>=1.16.5
networkx >=2.3
matplotlib>=3.1.1
pip install mesa-SIR
pip install git+https://github.com/metalcorebear/Mesa-SIR.git
Currently, Mesa SIR has two modules.
The main module is the SIR.py module. The SIR modules has two main parts.
The build network class has two parameters:
interactions = number of agents each agent will interact with each time step; integer
population = number of agents; integer
Using Mesa's NetworkGrid class this module builds a network of agents and their interactions and can be instantiated as follows.
from mesa_SIR import SIR
# returns a networkx graph to place in your Mesa NetworkGrid instance
G = SIR.build_network(interactions, population)
self.grid = NetworkGrid(G)
The infection class has two main part interactions and infection.
The infection class has the following key word arguments (kwargs):
- ptrans = 0.25 #probability of transmission; float between 0 and 1
- reinfection_rate = 0.00 #probability of reinfection; float between 0 and 1
- I0 =0.10 # initial infection rate; float between 0 and 1
- severe =0.18 # probability of severity; float between 0 and 1
- progression_period = 3 #number of days for disease to progress and show symptoms-- mean of a Gaussian distribution
- progression_sd = 2 #standard deviation of disease progression -- sd of a Gaussian distribution
- death_rate = 0.0193 #probability of disease being fatal
- recovery_days = 21 #number of days for disease to complete -- mean of Gaussian distribution
- recovery_sd = 7 #standard deviation of disease to finish -- sd of Gaussian distribution
The infection class can be instantiated as follows in the model initiation:
self.SIR_instance = SIR.Infection(self, ptrans = 0.40,
reinfection_rate = 0.10,
I0= 0.02,
severe = 0.90,
progression_period = 5,
progression_sd = 2,
death_rate = 0.30,
recovery_days = 38,
recovery_sd = 6
Due to the requirements of the infection model an agent needs several attributes. An example agent class instantiation with the required attributes is as follows. This initiation uses the Infection class' initial_infection method.
class human(Agent):
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.pos = unique_id
# Infection class initial infection method
self.was_infected = False
self.infected, self.susceptible, self.severe = self.model.SIR_instance.initial_infection()
self.recovered = False
self.alive = True
self.day = 0
self.induced_infections = 0
self.infected_others = False
Agent interaction and infection are then executed in the Infection class interaction method as follows:
def step(self):
self.model.SIR_instance.interact(self)
self.day += 1
This module conducts several computations and produces plots and data for the model.
An example of how to call the collections and plots modules and use with Mesa's datacollection module is as follows.
During the model initiation phase:
from mesa_SIR import calculations_and_plots as c_and_p
self.datacollector = DataCollector(model_reporters={"infected": lambda m: c_p.compute(m,'infected'),
"recovered": lambda m: c_p.compute(m,'recovered'),
"susceptible": lambda m: c_p.compute(m,"susceptible"),
"dead": lambda m: c_p.compute(m, "dead"),
"R0": lambda m: c_p.compute(m, "R0"),
"severe_cases": lambda m: c_p.compute(m,"severe")})
To save the dataframe and conduct the plots after the model is complete:
output_data = SIR_model.datacollector.get_model_vars_dataframe()
#Save the model dataframe
c_p.save_data(output_data) optional keywords: output_path, filename
#Create and save plots
title = 'SIR ABM Model Output'
c_p.plot_SIR(output_data, title) optional keyword: output_path
c_p.plot_R0(output_data, title) optional keyword: output_path
c_p.plot_severe(output_data, title) optional keyword: output_path
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent