A simple correction for COVID-19 sampling bias

Daniel Andrés Díaz-Pachón, J. Sunil Rao

Research output: Contribution to journalArticlepeer-review

Abstract

COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error.

Original languageEnglish (US)
Article number110556
JournalJournal of theoretical biology
Volume512
DOIs
StatePublished - Mar 7 2021

Keywords

  • Entropy
  • Epidemic
  • Estimation of prevalence
  • Outbreak
  • Symptoms

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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