OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing.
PLoS Comput Biol
; 17(7): e1009146, 2021 07.
Article
in English
| MEDLINE | ID: covidwho-1305573
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Systems Analysis
/
Contact Tracing
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pcbi.1009146
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