Openabm-Covid19 - an Agent-Based Model for Non-Pharmaceutical Interventions Against COVID-19 Including Contact Tracing
medRxiv preprint doi: https://doi.org/10.1101/2020.09.16.20195925; this version posted September 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. OpenABM-Covid19 - an agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing 1,* 1,* 1 1 1 Robert Hinch , William J M Probert , Anel Nurtay , Michelle Kendall , Chris Wymant , 1 1 1 1 1 Matthew Hall , Katrina Lythgoe , Ana Bulas Cruz , Lele Zhao , Andrea Stewart , Luca 1 2 2 2 3 3 Ferretti , Daniel Montero , James Warren , Nicole Mather , Matthew Abueg , Neo Wu , 4 1,5 1 1,6 Anthony Finkelstein , David G Bonsall , Lucie Abeler-Dörner , Christophe Fraser 1 Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK. 2 IBM United Kingdom, Portsmouth, UK 3 Google Research, Mountain View, CA, USA 4 Department of Computer Science, University College London, London, UK, and Alan Turing Institute, London, UK. 5 Oxford University NHS Trust, University of Oxford, Oxford, UK. 6 Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. * these authors contributed equally to this work 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 models being used to predict the spread of infection and assess the impact of public health measures.
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