covasim 3.1.6

About Covasim

Covasim is a stochastic agent-based simulator for performing COVID-19 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, quarantine, and vaccination.

The original scientific paper describing Covasim is available at http://paper.covasim.org. The recommended citation is:

Covasim: an agent-based model of COVID-19 dynamics and interventions. Kerr CC, Stuart RM, Mistry D, Abeysuriya RG, Rosenfeld R, Hart G, Núñez RC, Cohen JA, Selvaraj P, Hagedorn B, George L, Jastrzębski M, Izzo A, Fowler G, Palmer A, Delport D, Scott N, Kelly S, Bennette C, Wagner B, Chang S, Oron AP, Wenger E, Panovska-Griffiths J, Famulare M, Klein DJ (2021). PLOS Computational Biology 17 (7): e1009149. doi: https://doi.org/10.1371/journal.pcbi.1009149.

Covasim’s immunity module (including vaccines and variants) is described here:

Mechanistic modeling of SARS-CoV-2 immune memory, variants, and vaccines. Cohen JA, Stuart RM, Núñez RC, Wagner B, Chang ST, Rosenfeld K, Kerr CC, Famulare M, Klein DJ (under review; posted 2021-06-01). medRxiv 2021.05.31.21258018; doi: https://doi.org/10.1101/2021.05.31.21258018.

The Covasim webapp is available at https://app.covasim.org, and the repository for it is available here.

Covasim was developed by the Institute for Disease Modeling, with additional contributions from the University of Copenhagen, the Burnet Institute, GitHub, and Microsoft.

Questions or comments can be directed to info@covasim.org, or on this project’s GitHub page. Full information about Covasim is provided in the documentation.

Background

Covasim has been used for analyses in over a dozen countries, both to inform policy decisions (including in the US, UK, and Australia), and as part of research studies. Some key papers that have been written using Covasim include:

1. Controlling COVID-19 via test-trace-quarantine. Kerr CC, Mistry D, Stuart RM, Rosenfeld R, Hart G, Núñez RC, Selvaraj P, Cohen JA, Abeysuriya RG, George L, Hagedorn B, Jastrzębski M, Fagalde M, Duchin J, Famulare M, Klein DJ (2021). Nature Communications 12:2993. doi: https://doi.org/10.1038/s41467-021-23276-9.

2. Determining the optimal strategy for reopening schools, the impact of test and trace interventions, and the risk of occurrence of a second COVID-19 epidemic wave in the UK: a modelling study. Panovska-Griffiths J, Kerr CC, Stuart RM, Mistry D, Klein DJ, Viner R, Bonnell C (2020-08-03). Lancet Child and Adolescent Health S2352-4642(20) 30250-9. doi: https://doi.org/10.1016/S2352-4642(20)30250-9.

3. Estimating and mitigating the risk of COVID-19 epidemic rebound associated with reopening of international borders in Vietnam: a modelling study. Pham QD, Stuart RM, Nguyen TV, Luong QC, Tran DQ, Phan LT, Dang TQ, Tran DN, Mistry D, Klein DJ, Abeysuriya RG, Oron AP, Kerr CC (2021-04-12). Lancet Global Health S2214-109X(21) 00103-0; doi: https://doi.org/10.1016/S2214-109X(21)00103-0.

A more complete list of papers is given in papers.rst.

If you’ve written a paper or report using Covasim, we’d love to know about it! Please write to us here.

Requirements

Python 3.9-3.11 (64-bit). (Note: Python 2.7 and Python 3.12 are not supported, the latter being due to Numba not supporting Python 3.12 at the time of writing.)

We also recommend, but do not require, installing Covasim in a virtual environment. For more information, see documentation for e.g. 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()

Full installation instructions

If you would rather download the source code rather than using the pip package, follow these steps:

1. Clone a copy of the repository. If you intend to make changes to the code, we recommend that you fork it first.

2. (Optional) Create and activate a virtual environment.

3. Navigate to the root of the repository and install the Covasim Python package using one of the following options:

   • For normal installation (recommended):

     pip install -e .

   • To install Covasim and optional dependencies (be aware this may fail since it relies on nonstandard packages):

     pip install -e .[full]

   The module should then be importable via import covasim as cv.

Usage examples

There are several examples in the examples folder. 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.

Other examples in that folder are taken from the tutorials.

Module structure

All core model code is located in the covasim subfolder; standard usage is import covasim as cv. The data subfolder is 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, roughly 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.

• settings.py: User-customizable options for Covasim (e.g. default font size).

• defaults.py: The default colors, plots, etc. used by Covasim.

• parameters.py: Functions for creating the parameters dictionary and loading the input data.

• 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.

• 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.

• immunity.py: The strain class, and functions for computing waning immunity and neutralizing antibodies.

• 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 Analyzers class (for performing analyses on the sim while it’s running), the Fit class (for calculating the fit between the model and the data), the TransTree class, and other classes and functions for analyzing simulations.

The data folder within the Covasim package 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.

Other folders

Please see the readme in each subfolder for more information.

covasim --pars "{pop_size:20000, pop_infected:1, n_days:360, rand_seed:1}"

Disclaimer

The code in this repository was developed by IDM, the Burnet Institute, the University of Copenhagen, and other collaborators to support our joint research on COVID. We’ve made it publicly available under the MIT License to provide others with a better understanding of our research and an opportunity to build upon it for their own work. Note that Covasim depends on a number of user-installed Python packages that can be installed automatically via pip install. 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 MIT License. See the contributing and code of conduct READMEs for more information.