Forecasting the Spread of COVID-19 under Different Reopening Strategies
Liu, Meng, Thomadsen, Raphael, and Yao, Song. Forecasting the spread of COVID-19 under different reopening strategies. Sci Rep 10, 20367 (2020). https://doi.org/10.1038/s41598-020-77292-8
17 Pages Posted: 15 Jun 2020 Last revised: 23 Nov 2020
Date Written: October 28, 2020
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 contagious 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 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.
Note: Funding: We hereby declare that none of the authors received external funding
for this manuscript.
Declaration of Interest: None of the authors has any competing interest in developing this study.
Keywords: COVID-19, SIR Models, Social Distancing, Reopening Economy
Suggested Citation: Suggested Citation