Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation

Disaster Med Public Health Prep. 2020 Jun;14(3):364-371. doi: 10.1017/dmp.2020.298. Epub 2020 Aug 12.


In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.

Keywords: COVID-19; case fatality rate; coronavirus mortality overestimation; infection fatality rate; sampling bias.

MeSH terms

  • Bias*
  • COVID-19
  • Congresses as Topic / legislation & jurisprudence
  • Coronavirus Infections / epidemiology
  • Coronavirus Infections / mortality*
  • Humans
  • Mortality / trends*
  • Pandemics
  • Pneumonia, Viral / epidemiology
  • Pneumonia, Viral / mortality*
  • Public Health / methods
  • Public Health / trends
  • Statistics as Topic / methods
  • Statistics as Topic / standards*
  • Statistics as Topic / trends