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OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

Hinch, Robert ; Probert, William J M ; Nurtay, Anel ; Kendall, Michelle ; Wymant, Chris ; Hall, Matthew ; Lythgoe, Katrina ; Bulas Cruz, Ana ; Zhao, Lele ; Stewart, Andrea ; Ferretti, Luca ; Montero, Daniel ; Warren, James ; Mather, Nicole ; Abueg, Matthew ; Wu, Neo ; Legat, Olivier ; Bentley, Katie ; Mead, Thomas ; Van-Vuuren, Kelvin ; Feldner-Busztin, Dylan ; Ristori, Tommaso ; Finkelstein, Anthony ; Bonsall, David G ; Abeler-Dörner, Lucie ; Fraser, Christophe Althouse, Benjamin Muir

PLoS computational biology, 2021-07, Vol.17 (7), p.e1009146-e1009146 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing
Autor: Hinch, Robert ; Probert, William J M ; Nurtay, Anel ; Kendall, Michelle ; Wymant, Chris ; Hall, Matthew ; Lythgoe, Katrina ; Bulas Cruz, Ana ; Zhao, Lele ; Stewart, Andrea ; Ferretti, Luca ; Montero, Daniel ; Warren, James ; Mather, Nicole ; Abueg, Matthew ; Wu, Neo ; Legat, Olivier ; Bentley, Katie ; Mead, Thomas ; Van-Vuuren, Kelvin ; Feldner-Busztin, Dylan ; Ristori, Tommaso ; Finkelstein, Anthony ; Bonsall, David G ; Abeler-Dörner, Lucie ; Fraser, Christophe
Althouse, Benjamin Muir
Assuntos: Age ; Age groups ; Agent based models ; Biology and Life Sciences ; Computer applications ; Contact Tracing ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; COVID-19 - prevention & control ; COVID-19 - transmission ; COVID-19 - virology ; COVID-19 Testing ; COVID-19 Vaccines - administration & dosage ; Demographics ; Demography ; Disease Outbreaks ; Disease transmission ; Economic conditions ; Epidemics ; Health services ; Households ; Humans ; Infection control ; Infections ; Interfaces ; Intervention ; Mathematical models ; Medicine and Health Sciences ; Modularity ; Older people ; Pandemics ; Parameterization ; Pathogens ; People and Places ; Pharmaceuticals ; Physical Distancing ; Population ; Public health ; Public transportation ; Quarantine ; SARS-CoV-2 - isolation & purification ; Severe acute respiratory syndrome coronavirus 2 ; Simulation ; Social networks ; Social organization ; Statistical inference ; Statistical models ; Systems Analysis ; Vaccination ; Viral diseases
É parte de: PLoS computational biology, 2021-07, Vol.17 (7), p.e1009146-e1009146
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I have read the journal’s policy and the authors of this manuscript have the following competing interests: M.A., N.W., and O.L. are employees of Alphabet, Inc., a provider of the Exposure Notification System; no other relationships or activities that could appear to have influenced the submitted work. All other authors have declared that no competing interests exist.
Descrição: 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 epidemic.
Editor: United States: Public Library of Science
Idioma: Inglês

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OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

United States: Public Library of Science

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