TY - JOUR
T1 - OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
AU - Hinch, Robert
AU - Probert, William J.M.
AU - Nurtay, Anel
AU - Kendall, Michelle
AU - Wymant, Chris
AU - Hall, Matthew
AU - Lythgoe, Katrina
AU - Bulas Cruz, Ana
AU - Zhao, Lele
AU - Stewart, Andrea
AU - Ferretti, Luca
AU - Montero, Daniel
AU - Warren, James
AU - Mather, Nicole
AU - Abueg, Matthew
AU - Wu, Neo
AU - Legat, Olivier
AU - Bentley, Katie
AU - Mead, Thomas
AU - Van-Vuuren, Kelvin
AU - Feldner-Busztin, Dylan
AU - Ristori, Tommaso
AU - Finkelstein, Anthony
AU - Bonsall, Dav G.
AU - Abeler-Dörner, Lucie
AU - Fraser, Christophe
N1 - Publisher Copyright:
© 2021 Hinch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/7
Y1 - 2021/7
N2 - SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: An agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19
AB - SARS-CoV-2 has spread across the world, causing high mortality and unprecedented restrictions on social and economic activity. Policymakers are assessing how best to navigate through the ongoing epidemic, with computational models being used to predict the spread of infection and assess the impact of public health measures. Here, we present OpenABM-Covid19: An agent-based simulation of the epidemic including detailed age-stratification and realistic social networks. By default the model is parameterised to UK demographics and calibrated to the UK epidemic, however, it can easily be re-parameterised for other countries. OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. It can simulate a population of 1 million people in seconds per day, allowing parameter sweeps and formal statistical model-based inference. The code is open-source and has been developed by teams both inside and outside academia, with an emphasis on formal testing, documentation, modularity and transparency. A key feature of OpenABM-Covid19 are its Python and R interfaces, which has allowed scientists and policymakers to simulate dynamic packages of interventions and help compare options to suppress the COVID-19
UR - http://www.scopus.com/inward/record.url?scp=85110953504&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1009146
DO - 10.1371/journal.pcbi.1009146
M3 - Article
AN - SCOPUS:85110953504
SN - 1553-734X
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 7
M1 - e1009146
ER -