Forecasting the spread of COVID-19 under different reopening strategies

Abstract We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expecte...

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Autores principales:	Meng Liu, Raphael Thomadsen, Song Yao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/412bc89f075d4fceb28bfed68ceb49eb
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spelling	oai:doaj.org-article:412bc89f075d4fceb28bfed68ceb49eb2021-12-02T16:08:37ZForecasting the spread of COVID-19 under different reopening strategies10.1038/s41598-020-77292-82045-2322https://doaj.org/article/412bc89f075d4fceb28bfed68ceb49eb2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77292-8https://doaj.org/toc/2045-2322Abstract We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.Meng LiuRaphael ThomadsenSong YaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Meng Liu Raphael Thomadsen Song Yao Forecasting the spread of COVID-19 under different reopening strategies
description	Abstract We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.
format	article
author	Meng Liu Raphael Thomadsen Song Yao
author_facet	Meng Liu Raphael Thomadsen Song Yao
author_sort	Meng Liu
title	Forecasting the spread of COVID-19 under different reopening strategies
title_short	Forecasting the spread of COVID-19 under different reopening strategies
title_full	Forecasting the spread of COVID-19 under different reopening strategies
title_fullStr	Forecasting the spread of COVID-19 under different reopening strategies
title_full_unstemmed	Forecasting the spread of COVID-19 under different reopening strategies
title_sort	forecasting the spread of covid-19 under different reopening strategies
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/412bc89f075d4fceb28bfed68ceb49eb
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Forecasting the spread of COVID-19 under different reopening strategies

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