---
id: engines/june
about:
  label: JUNE
  description: >-
    Open-source framework, kept by the IDAS-Durham group, that simulates
    epidemics through the daily life of a virtual population built from
    fine-grained census data; first applied to COVID-19 in England.
  tags:
    - epidemic model
    - agent-based
    - synthetic population
    - census
    - England
tool:
  form: open-source Python framework
  group: People and populations
  homepage: 'https://github.com/IDAS-Durham/JUNE'
  licence: GPL-3.0
  cost: free; needs an MPI installation
domains:
  - people
  - places
open:
  source: open
  data: open
  api: 'yes'
  participation: open
  licence: GPL-3.0
  note: >-
    Input data for England is built from Office for National Statistics tables
    and downloaded by a script.
relations:
  - predicate: alternative_to
    target: engines/openabm-covid19
provenance:
  origin: agent
  sources:
    - title: IDAS-Durham/JUNE (README)
      url: 'https://github.com/IDAS-Durham/JUNE'
    - title: >-
        June: open-source individual-based epidemiology simulation (Crossref
        record)
      url: 'https://api.crossref.org/works/10.1098/rsos.210506'
  created: '2026-10-23'
  reviewed: '2026-10-23'
  reviewed_by: 'Claude, from the sources listed'
  status: draft
---
JUNE builds a virtual population of a country from census data at fine geographic detail, with each person given an age, sex, ethnicity and socio-economic indicators.[^2] An infection then spreads through the groups they spend their days in: households, schools, workplaces and other social activities.[^2] How people mix within each group follows social mixing matrices.[^2] Diseases are described by a set of parameters, so the framework is not tied to one virus.[^2] It is named after June Almeida, the Scottish virologist who first identified the coronavirus group.[^1]

The code is kept by the IDAS-Durham group on GitHub, and the framework was described in Royal Society Open Science in July 2021 by Joseph Aylett-Bullock and thirteen co-authors.[^1,2] That paper applied it to COVID-19 in England and reports that its first outputs reproduced hospital admissions and deaths at national and regional level and by age group.[^2]

It installs from PyPI as `june`, and a script downloads the input data for England.[^1] That data mixes several Office for National Statistics datasets and may contain modifications; the original sources are cited in the release paper.[^1] Running it needs an MPI installation, the software that splits a large run across many processors, and the README carries notes for two computing clusters.[^1] A quickstart notebook introduces the model.[^1]

Development has slowed: the last tagged release was version 1.2.0 in October 2022.[^1] Using it for a country other than England means building a new population from that country's census first. The code is GPL-3.0.[^1]