Covasim: COVID-19 Agent-Based Simulator

Overview

Covasim is an open-source stochastic agent-based model of COVID-19 dynamics and interventions, written in Python. It is designed to project epidemic trends and evaluate intervention scenarios at scales ranging from individual cities to entire countries. Covasim incorporates demographic data, realistic transmission networks across social settings (households, schools, workplaces, communities, and long-term care facilities), age-specific disease outcomes, and viral dynamics including immunity and variants. It has been used by researchers and public health officials in over a dozen countries to inform policy decisions.

Key features

  • Agent-based: Each individual in the simulated population is represented as a distinct agent with their own disease state, contacts, and history.
  • Stochastic: Transmission events and disease outcomes incorporate randomness, enabling uncertainty quantification across multiple runs.
  • Realistic networks: Transmission occurs across configurable contact layers — households, schools, workplaces, communities, and long-term care facilities.
  • Interventions: Supports physical distancing, mask use, testing, contact tracing, isolation, quarantine, school closures, and vaccination.
  • Immunity and variants: Built-in immunity module covers vaccine-derived and infection-derived immunity, waning, and multiple SARS-CoV-2 variants.
  • Calibration: Built-in tools for fitting the model to epidemiological data.
  • Fast: Realistic scenarios run in under a minute on a standard laptop.

Publications

The scientific paper describing Covasim was published in 2021 in PLOS Computational Biology. The recommended citation is:

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

A companion paper describing the immunity, variants, and vaccines module is:

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

Papers from the Covasim development team

Academic papers that have used Covasim, authored by the core development team:

  1. 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 (2020). Panovska-Griffiths J, Kerr CC, Stuart RM, Mistry D, Klein DJ, Viner R, Bonnell C. Lancet Child and Adolescent Health. https://doi.org/10.1016/S2352-4642(20)30250-9

  2. Modelling the impact of reducing control measures on the COVID-19 pandemic in a low transmission setting (2020). Scott N, Palmer A, Delport D, Abeysuriya RG, Stuart RM, Kerr CC, Mistry D, Klein DJ, Sacks-Davis R, Heath K, Hainsworth S, Pedrana A, Stoove M, Wilson DP, Hellard M. Medical Journal of Australia. https://doi.org/10.1101/2020.06.11.20127027

  3. Modelling the potential impact of mask use in schools and society on COVID-19 control in the UK (2020). Panovska-Griffiths J, Kerr CC, Waites W, Stuart RM, Mistry D, Foster D, Klein DJ, Viner R, Bonnell C. Scientific Reports. https://doi.org/10.1038/s41598-021-88075-0

  4. Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures (2020). Cohen JA, Mistry D, Kerr CC, Klein DJ. medRxiv 2020.09.08.20190942. https://doi.org/10.1101/2020.09.08.20190942

  5. COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support (2020). Shea K, Borchering RK, Probert WJM, et al. medRxiv 2020.11.03.20225409. https://doi.org/10.1101/2020.11.03.20225409

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

  7. The role of masks, testing and contact tracing in preventing COVID-19 resurgences: a case study from New South Wales, Australia (2021). Stuart RM, Abeysuriya RG, Kerr CC, Mistry D, Klein DJ, Gray R, Hellard M, Scott N. BMJ Open. https://doi.org/10.1101/2020.09.02.20186742

  8. Estimating and mitigating the risk of COVID-19 epidemic rebound associated with reopening of international borders in Vietnam: a modelling study (2021). 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. Lancet Global Health. https://doi.org/10.1016/S2214-109X(21)00103-0

  9. Modelling the impact of reopening schools in early 2021 in the presence of the new SARS-CoV-2 variant in the UK (2021). Panovska-Griffiths J, Kerr CC, Waites W, Stuart RM, Mistry D, Foster D, Klein DJ, Viner R, Bonnell C. medRxiv 2021.02.07.21251287. https://doi.org/10.1101/2021.02.07.21251287

  10. Preventing a cluster from becoming a new wave in settings with zero community COVID-19 cases (2022). Abeysuriya RG, Delport D, Stuart RM, Sacks-Davis R, Kerr CC, Mistry D, Klein DJ, Hellard M, Scott N. BMC Infectious Diseases, 22(1):1–5. https://doi.org/10.1186/s12879-022-07180-1

Papers from external authors

Academic papers from researchers outside the core team that have used Covasim:

  1. Partial lockdown on unvaccinated individuals promises breaking of fourth COVID-19 wave in Bavaria (2021). Krebs T, Moeckel MJ. medRxiv 2021.11.28.21266959. https://doi.org/10.1101/2021.11.28.21266959

  2. Returning to a normal life via COVID-19 vaccines in the United States: a large-scale agent-based simulation study (2021). Li J, Giabbanelli P. JMIR Medical Informatics, 9(4):e27419.

  3. An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: the case study of Lombardy region (2022). Cattaneo A, Vitali A, Mazzoleni M, Previdi F. Computer Methods and Programs in Biomedicine, 224:107029.

  4. Estimation of local time-varying reproduction numbers in noisy surveillance data (2022). Li W, Bulekova K, Gregor B, White LF, Kolaczyk ED. Philosophical Transactions of the Royal Society A, 380(2233):20210303.

  5. A simulation-deep reinforcement learning (SiRL) approach for epidemic control optimization (2022). Bushaj S, Yin X, Beqiri A, Andrews D, Büyüktahtakın İE. Annals of Operations Research.

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