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Forecasting the spread of COVID-19 under different reopening strategies

Medicine and Health

Forecasting the spread of COVID-19 under different reopening strategies

M. Liu, R. Thomadsen, et al.

This groundbreaking research conducted by Meng Liu, Raphael Thomadsen, and Song Yao combines COVID-19 case data with mobility data to provide new insights into the dynamics of infection spread in the United States. Their modified SIR model reveals a surprising concavity in spread related to the number of infectious individuals, significantly influencing future case forecasts. Discover what happens when social distancing is lifted!

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Playback language: English
Abstract
This paper combines COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the United States. The model reveals that the incidence of COVID-19 spread is concave in the number of infectious individuals, suggesting interconnected social networks. This concavity significantly impacts COVID-19 case forecasts, predicting an initial period of exponential growth followed by a prolonged period of stable, slightly declining spread. Simulations under various social distancing scenarios forecast a massive increase in cases if social distancing is eliminated.
Publisher
Scientific Reports
Published On
Oct 27, 2020
Authors
Meng Liu, Raphael Thomadsen, Song Yao
Tags
COVID-19
SIR model
infectious individuals
social distancing
mobility data
case forecasts

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