Aylett-Bullock, J. and Cuesta-Lazaro, C. and Quera-Bofarull, A. and Icaza-Lizaola, M. and Sedgewick, A. and Truong, H. and Curran, A. and Elliott, E. and Caulfield, T. and Fong, K. and Vernon, I. and Williams, J. and Bower, R. and Krauss, F. (2021) 'JUNE: open-source individual-based epidemiology simulation.', Royal Society open science., 8 (7).
We introduce June, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. June provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply June to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.
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|Publisher Web site:||https://doi.org/10.1098/rsos.210506|
|Publisher statement:||© 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.|
|Date accepted:||22 June 2021|
|Date deposited:||23 July 2021|
|Date of first online publication:||07 July 2021|
|Date first made open access:||23 July 2021|
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