COVID-19 Agent-based Simulator
Project description
Covasim is a stochastic agent-based simulator designed to be used for COVID-19 (novel coronavirus, SARS-CoV-2) epidemic analyses. These include projections of indicators such as numbers of infections and peak hospital demand. Covasim can also be used to explore the potential impact of different interventions, including social distancing, school closures, testing, contact tracing, and quarantine.
The scientific paper describing Covasim is available at http://paper.covasim.org. The recommended citation is:
Kerr CC, Stuart RM, Mistry D, Abeysuriya RG, Hart G, Rosenfeld R, Selvaraj P, Núñez RC, Hagedorn B, George L, Izzo A, Palmer A, Delport D, Bennette C, Wagner B, Chang S, Cohen JA, Panovska-Griffiths J, Jastrzębski M, Oron AP, Wenger E, Famulare M, Klein DJ (2020). Covasim: an agent-based model of COVID-19 dynamics and interventions. medRxiv 2020.05.10.20097469; doi: https://doi.org/10.1101/2020.05.10.20097469.
The Covasim webapp is available at http://app.covasim.org.
Questions or comments can be directed to covasim@idmod.org, or on this project’s GitHub page. Full information about Covasim is provided in the documentation.
Requirements
Python >=3.6 (64-bit). (Note: Python 2 is not supported.)
We also recommend, but do not require, using Python virtual environments. For more information, see documentation for venv or Anaconda.
Quick start guide
Install with pip install covasim. If everything is working, the following Python commands should bring up a plot:
import covasim as cv sim = cv.Sim() sim.run() sim.plot()
Detailed installation instructions
Clone a copy of the repository. If you intend to make changes to the code, we recommend that you fork it first.
(Optional) Create and activate a virtual environment.
Navigate to the root of the repository and install the Covasim Python package using one of the following options:
To install with web app support (recommended):
python setup.py develop
To install as a standalone Python model without webapp support:
python setup.py develop nowebapp
To install Covasim and optional dependencies (be aware this may fail since it relies on private packages), enter:
python setup.py develop full
The module should then be importable via import covasim.
Usage examples
There are several examples in the examples directory. These can be run as follows:
python examples/simple.py
This example creates a figure using default parameter values.
python examples/run_sim.py
This shows a slightly more detailed example, including creating an intervention and saving to disk.
python examples/run_scenarios.py
This shows a more complex example, including running an intervention scenario, plotting uncertainty, and performing a health systems analysis.
Module structure
All core model code is located in the covasim subfolder; standard usage is import covasim as cv. The other subfolders, data, and webapp, are also described below.
The model consists of two core classes: the Person class (which contains information on health state), and the Sim class (which contains methods for running, calculating results, plotting, etc.).
The structure of the covasim folder is as follows, in the order in which the modules are imported, building from most fundamental to most complex:
version.py: Version, date, and license information.
requirements.py: A simple module to check that imports succeeded, and turn off features if they didn’t.
utils.py: Functions for choosing random numbers, many based on Numba, plus other helper functions.
misc.py: Miscellaneous helper functions.
defaults.py: The default colors, plots, etc. used by Covasim.
plotting.py: Plotting scripts, including Plotly graphs for the webapp (used in other Covasim classes, and hence defined first).
base.py: The ParsObj class, the fundamental class used in Covasim, plus basic methods of the BaseSim and BasePeople classes, and associated functions.
parameters.py: Functions for creating the parameters dictionary and loading the input data.
people.py: The People class, for handling updates of state for each person.
population.py: Functions for creating populations of people, including age, contacts, etc.
interventions.py: The Intervention class, for adding interventions and dynamically modifying parameters, and classes for each of the specific interventions derived from it.
sim.py: The Sim class, which performs most of the heavy lifting: initializing the model, running, and plotting.
run.py: Functions for running simulations (e.g. parallel runs and the Scenarios and MultiSim classes).
analysis.py: The TransTree class, and other classes and functions for analyzing simulations.
Data
This folder contains loading scripts for the epidemiological data in the root data folder, as well as data on age distributions for different countries and household sizes.
Webapp
For running the interactive web application. See the webapp README for more information.
Other folders
Please see the readme in each subfolder for more information.
Bin
This folder contains a command-line interface (CLI) version of Covasim; example usage:
covasim --pars "{pop_size:20000, pop_infected:1, n_days:360, rand_seed:1}"
Note: the CLI is currently not compatible with Windows. You will need to add this folder to your path to run from other folders. See the bin README for more information.
Data
Scripts to automatically scrape data (including demographics and COVID epidemiology data), and the data files themselves (which are not part of the repository). See the data README for more information.
Docker
This folder contains the Dockerfile and other files that allow Covasim to be run as a webapp via Docker. See the Docker README for more information.
Examples
This folder contains demonstrations of simple Covasim usage, including an early application of Covasim to the Diamond Princess cruise ship. See the examples README for more information.
Licenses
Licensing information and legal notices.
Tests
Integration, development, and unit tests. While not (yet) beautifully curated, these folders contain many usage examples. See the tests README for more information.
Sweep
Utilities for hyperparameter sweeps, using Weights and Biases. See the sweep README for more information.
Disclaimer
The code in this repository was developed by IDM to support our research in disease transmission and managing epidemics. We’ve made it publicly available under the Creative Commons Attribution-Noncommercial-ShareAlike 4.0 License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. We make no representations that the code works as intended or that we will provide support, address issues that are found, or accept pull requests. You are welcome to create your own fork and modify the code to suit your own modeling needs as contemplated under the Creative Commons Attribution-Noncommercial-ShareAlike 4.0 License. See the contributing and code of conduct READMEs for more information.
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