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June: open-source individual-based epidemiology simulation.

Joseph Aylett-Bullock1,2, Carolina Cuesta-Lazaro1,3, Arnau Quera-Bofarull1,3

  • 1Institute for Data Science, Durham University, Durham DH1 3LE, UK.

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Summary
This summary is machine-generated.

We developed June, an open-source epidemic simulation framework using detailed social interactions and census data. This tool accurately models COVID-19 spread in England, matching national and regional health statistics.

Keywords:
individual-based modelinfectious diseasesimulation

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health Modeling

Background:

  • Epidemic simulation requires realistic population data and social interaction models.
  • Existing frameworks may lack the granularity for detailed, geographically specific disease spread analysis.

Purpose of the Study:

  • To introduce June, an open-source framework for detailed epidemic simulation.
  • To apply June to model COVID-19 transmission dynamics in England.
  • To validate the model's accuracy against real-world health data.

Main Methods:

  • Constructed a virtual population using geographically granular census data (age, sex, ethnicity, socio-economic indicators).
  • Modeled individual interactions within social groups (households, schools, workplaces) using social mixing matrices.
  • Developed flexible parametrizations for disease characteristics, transmission, and individual effects.

Main Results:

  • June successfully simulated COVID-19 spread in England.
  • Initial model outputs closely reproduced reported national and regional hospital admission and mortality statistics.
  • Model performance was validated across different age strata.

Conclusions:

  • June provides a robust and flexible framework for detailed epidemic simulation.
  • The model demonstrates high fidelity in replicating key epidemiological outcomes for COVID-19 in England.
  • June can be a valuable tool for public health policy and preparedness.