Dynamic interplay between social distancing duration and intensity in reducing COVID-19 US hospitalizations: A "law of diminishing returns"

Chaos. 2020 Jul;30(7):071102. doi: 10.1063/5.0013871.

Abstract

We uncover and highlight the importance of social distancing duration and intensity in lowering hospitalization demand-to-supply during the coronavirus disease 2019 (COVID-19) epidemic in the USA. We have developed an epidemic progression model involving the susceptible-exposed-infected-recovered dynamics, the age-stratified disease transmissibility, and the possible large-scale undocumented (i.e., asymptomatic and/or untested) transmission of COVID-19 taking place in the USA. Our analysis utilizes COVID-19 observational data in the USA between March 19 and 28, corresponding to the early stage of the epidemic when the impacts of social distancing on disease progression were yet to manifest. Calibrating our model using epidemiological data from this time period enabled us to unbiasedly address the question "How long and with what intensity does the USA need to implement social distancing intervention during the COVID-19 pandemic?" For a short (i.e., up to two weeks) duration, we find a near-linear decrease in hospital beds demand with increasing intensity (φ) of social distancing. For a duration longer than two weeks, our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. Long-term implementation of strict social distancing with φ>50% could lead to the emergence of a second wave of infections due to a large residual susceptible population which highlights the need for contact tracing and isolation before re-opening of the economy. Finally, we investigate the scenario of intermittent social distancing and find an optimal social-to-no-distancing duration ratio of 5:1 corresponding to a sustainable reduction in medical demands.

MeSH terms

  • Algorithms
  • Betacoronavirus
  • COVID-19
  • Calibration
  • Coronavirus Infections / epidemiology*
  • Coronavirus Infections / prevention & control*
  • Disease Progression
  • Epidemics
  • Hospitalization / statistics & numerical data*
  • Humans
  • Models, Theoretical
  • Pandemics / prevention & control*
  • Pneumonia, Viral / epidemiology*
  • Pneumonia, Viral / prevention & control*
  • Public Health Informatics
  • Quarantine*
  • SARS-CoV-2
  • Social Isolation*
  • United States / epidemiology