Forecasting the spread of COVID-19 under different reopening strategies

Sci Rep. 2020 Nov 23;10(1):20367. doi: 10.1038/s41598-020-77292-8.


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.

MeSH terms

  • COVID-19 / epidemiology*
  • COVID-19 / prevention & control
  • COVID-19 / transmission*
  • COVID-19 / virology
  • Disease Susceptibility / epidemiology*
  • Disease Susceptibility / virology
  • Forecasting / methods
  • Humans
  • Humidity
  • Models, Statistical*
  • Pandemics*
  • Physical Distancing
  • Public Health
  • Quarantine / methods
  • SARS-CoV-2*
  • Temperature
  • United States / epidemiology