Open AccessJ une : open-source individual-based epidemiology simulation
Author(s) -
Joseph Aylett-Bullock,
Carolina Cuesta-Lázaro,
Arnau Quera-Bofarull,
Miguel Icaza-Lizaola,
Aidan Sedgewick,
Henry Truong,
Aoife Curran,
Edward Elliott,
Tristan Caulfield,
Kevin Fong,
Ian Ver,
Julian Williams,
Richard G. Bower,
Frank Krauss
Publication year - 2021
Publication title -
royal society open science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 51
ISSN - 2054-5703
DOI - 10.1098/rsos.210506
Subject(s) - suite , census , open source , computer science , geography , population , covid-19 , econometrics , demography , sociology , economics , medicine , infectious disease (medical specialty) , disease , archaeology , software , pathology , programming language
We introduce J une , 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. J une provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply J une 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.