A Statistical Model for Quantifying the Needed Duration of Social Distancing for the COVID-19 Pandemic

Publication Authors

Nadav Rakocz, Boyang Fu, Eran Halperin, Sriram Sankararaman

Published in KDD'20
Publication Date

Abstract

Understanding the effectiveness of strategies such as social distancing is a central question in attempts to control the COVID-19 pandemic. A key unknown in social distancing strategies is the duration of time for which such strategies are needed. Answering this question requires an accurate model of the transmission trajectory. A challenge in fitting such a model is the limited COVID-19 case data available from a given location. To overcome this challenge, we propose fitting a model of SARS-CoV-2 transmission jointly across multiple locations. We apply the model to COVID-19 case data from Spain, UK, Germany, France, Denmark, and New York to estimate the distribution for the time needed for social distancing to end to range from May 2020 to July 2021 (95% credible interval), where the median date is October 2020. Our method is not specific to COVID-19, and it can also be applied to future pandemics.

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