The challenges of modeling and forecasting the spread of COVID-19

Proc Natl Acad Sci U S A. 2020 Jul 21;117(29):16732-16738. doi: 10.1073/pnas.2006520117. Epub 2020 Jul 2.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.

Keywords: COVID-19; branching process; compartmental models; pandemic.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Betacoronavirus / pathogenicity*
  • COVID-19
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / prevention & control*
  • Coronavirus Infections / transmission*
  • Coronavirus Infections / virology
  • Humans
  • Infection Control / methods*
  • Infection Control / organization & administration*
  • Models, Theoretical*
  • Pandemics / prevention & control*
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / prevention & control*
  • Pneumonia, Viral / transmission*
  • Pneumonia, Viral / virology
  • Public Health
  • SARS-CoV-2
  • United States / epidemiology