---
id: engines/openabm-covid19
about:
  label: OpenABM-Covid19
  description: >-
    Open-source agent-based model of COVID-19 from the University of Oxford's
    Big Data Institute, with IBM UK and Faculty; simulates infection through
    household, workplace and random contacts and tests lockdowns, vaccination
    and manual and app-based contact tracing.
  tags:
    - epidemic model
    - agent-based
    - COVID-19
    - contact tracing
    - public health
tool:
  form: open-source C model with Python and R interfaces
  group: People and populations
  homepage: 'https://github.com/BDI-pathogens/OpenABM-Covid19'
  licence: GPL-3.0
  cost: free
domains:
  - people
open:
  source: open
  data: open
  api: 'yes'
  participation: open
  licence: GPL-3.0
relations:
  - predicate: alternative_to
    target: engines/covasim-starsim
provenance:
  origin: agent
  sources:
    - title: BDI-pathogens/OpenABM-Covid19 (README)
      url: 'https://github.com/BDI-pathogens/OpenABM-Covid19'
    - title: >-
        OpenABM-Covid19, An agent-based model for non-pharmaceutical
        interventions against COVID-19 including contact tracing (Crossref
        record)
      url: 'https://api.crossref.org/works/10.1371/journal.pcbi.1009146'
    - title: Releases · BDI-pathogens/OpenABM-Covid19
      url: 'https://github.com/BDI-pathogens/OpenABM-Covid19/releases'
  created: '2026-10-23'
  reviewed: '2026-10-23'
  reviewed_by: 'Claude, from the sources listed'
  status: draft
---
OpenABM-Covid19 simulates the spread of COVID-19 through a city. Each simulated person belongs to a household and a workplace and also meets random contacts, and the virus passes along these links.[^1] The model follows each infected person through the course of the disease.[^1] Its distinctive feature is contact tracing: it can trace and quarantine the contacts of a case instantly.[^1] The team used it to judge how a phone contact-tracing app should be configured.[^1]

It was built by the Pathogen Dynamics group at the Big Data Institute, University of Oxford, with IBM UK and the data science company Faculty.[^1] The model paper, by Hinch and colleagues, appeared in PLOS Computational Biology in July 2021.[^2] By default it is set up with UK demographics and calibrated to the UK epidemic, and it can be re-parameterised for other countries.[^2] It simulates a million people at a few seconds per simulated day, fast enough to run many variations and to fit the model to data statistically.[^2] A companion model, adaptER-covid19, estimates the economic effects alongside the epidemic.[^1]

The core is written in C and needs a compiler and the GNU Scientific Library; it is best run from Python, and it also has an R interface.[^1] Inputs are a table of parameters and a table of household make-ups to sample from, and the repository documents both, along with every output file and the model's test suite.[^1] Example notebooks show a lockdown triggered by case numbers followed by app-based tracing.[^1]

The last tagged release is version 1.0 from April 2021.[^3] It remains a clear, well-documented example of an epidemic model built around social networks. The code is GPL-3.0.[^1] For a maintained alternative in Python, see [Covasim and Starsim](/engines/covasim-starsim/).