Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts

Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts

Abstract Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made t...

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Bibliographic Details
Main Authors: Farah Houdroge, Anna Palmer, Dominic Delport, Tom Walsh, Sherrie L. Kelly, Samuel W. Hainsworth, Romesh Abeysuriya, Robyn M. Stuart, Cliff C. Kerr, Paul Coplan, David P. Wilson, Nick Scott
Format: Article
Language:English
Published: Nature Portfolio 2023-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-27711-3

Internet

https://doi.org/10.1038/s41598-023-27711-3

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