Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions

Science. 2020 Jul 10;369(6500):eabb9789. doi: 10.1126/science.abb9789. Epub 2020 May 15.

Abstract

As coronavirus disease 2019 (COVID-19) is rapidly spreading across the globe, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. A major challenge for short-term forecasts is the assessment of key epidemiological parameters and how they change when first interventions show an effect. By combining an established epidemiological model with Bayesian inference, we analyzed the time dependence of the effective growth rate of new infections. Focusing on COVID-19 spread in Germany, we detected change points in the effective growth rate that correlate well with the times of publicly announced interventions. Thereby, we could quantify the effect of interventions and incorporate the corresponding change points into forecasts of future scenarios and case numbers. Our code is freely available and can be readily adapted to any country or region.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • COVID-19 / drug therapy
  • COVID-19 / epidemiology*
  • Coronavirus Infections / drug therapy
  • Coronavirus Infections / epidemiology*
  • Forecasting
  • Germany / epidemiology
  • Humans
  • Pandemics
  • Pneumonia, Viral / drug therapy
  • Pneumonia, Viral / epidemiology*