How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

Emma K Accorsi, Xueting Qiu, Eva Rumpler, Lee Kennedy-Shaffer, Rebecca Kahn, Keya Joshi, Edward Goldstein, Mats J Stensrud, Rene Niehus, Muge Cevik, Marc Lipsitch

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In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.
Original languageEnglish
Number of pages18
JournalEuropean Journal of Epidemiology
Early online date25 Feb 2021
Publication statusE-pub ahead of print - 25 Feb 2021


  • COVID-19
  • Epidemiological biases
  • Measurement error
  • Misclassification
  • Observational data
  • Selection bias


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