Sampling bias minimization in disease frequency estimates

Oshrit Shtossel, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review


An accurate estimate of the number of infected individuals in any disease is crucial. Current estimates are mainly based on the fraction of positive samples or the total number of positive samples. However, both methods are biased and sensitive to the sampling depth. We here propose an alternative method to use the attributes of each sample to estimate the change in the total number of positive patients in the total population. We present a Bayesian estimator assuming a combination of condition and time-dependent probability of being positive, and mixed implicit-explicit solution for the probability of a person with conditions i at time t of being positive. We use this estimate to predict the total probability of being positive at a given day t. We show that these estimate results are smooth and not sensitive to the properties of the samples. Moreover, these results are a better predictor of future mortality.

Original languageEnglish
Article number110972
JournalJournal of Theoretical Biology
StatePublished - 7 Feb 2022


  • Bayesian statistics
  • COVID-19
  • Data analysis
  • Mathematical modeling

All Science Journal Classification (ASJC) codes

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


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