Aylett-Bullock, J;
Cuesta-Lazaro, C;
Quera-Bofarull, A;
Icaza-Lizaola, M;
Sedgewick, A;
Truong, H;
Curran, A;
... Krauss, F; + view all
(2021)
June: open-source individual-based epidemiology simulation.
Royal Society Open Science
, 8
(7)
, Article 210506. 10.1098/rsos.210506.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | June: open-source individual-based epidemiology simulation |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsos.210506 |
| Publisher version: | https://doi.org/10.1098/rsos.210506 |
| Language: | English |
| Additional information: | Copyright © 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. |
| Keywords: | simulation, infectious disease, individual-based model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10132387 |
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