Forecasting efforts from prior epidemics and COVID-19 predictions

Eur J Epidemiol. 2020 Aug;35(8):727-729. doi: 10.1007/s10654-020-00661-0. Epub 2020 Jul 17.

Abstract

Since the onset of the COVID-19 pandemic, countless disease prediction models have emerged, shaping the focus of news media, policymakers, and broader society. We reviewed the accuracy of forecasts made during prior twenty-first century epidemics, namely SARS, H1N1, and Ebola. We found that while disease prediction models were relatively nascent as a research focus during SARS and H1N1, for Ebola, numerous such forecasts were published. We found that forecasts of deaths for Ebola were often far from the eventual reality, with a strong tendency to over predict. Given the societal prominence of these models, it is crucial that their uncertainty be communicated. Otherwise, we will be unaware if we are being falsely lulled into complacency or unjustifiably shocked into action.

Keywords: COVID-19; Ebola; Forecasting; Pandemics; Predictions.

MeSH terms

  • Betacoronavirus
  • COVID-19
  • Coronavirus Infections* / epidemiology
  • Coronavirus Infections* / prevention & control
  • Coronavirus Infections* / transmission
  • Epidemics
  • Forecasting*
  • Hemorrhagic Fever, Ebola / epidemiology
  • Humans
  • Influenza A Virus, H1N1 Subtype
  • Influenza, Human / epidemiology
  • Models, Statistical
  • Pandemics*
  • Pneumonia, Viral* / epidemiology
  • Pneumonia, Viral* / prevention & control
  • Pneumonia, Viral* / transmission
  • SARS-CoV-2
  • Uncertainty