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Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing
Constantin T Yiannoutsos1, Paul K Halverson2, Nir Menachemi2,3
1Department of Biostatistics, Indiana University Fairbanks School of Public Health, Indianapolis, IN 46202; cyiannou@iu.edu.
Summary
Indiana
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- The COVID-19 pandemic necessitated rapid assessment of disease prevalence.
- Indiana conducted its first statewide randomized COVID-19 testing study in April 2020.
- Accurate prevalence estimation requires addressing nonresponse and testing errors.
Purpose of the Study:
- To describe statistical methods for adjusting COVID-19 prevalence estimates in Indiana.
- To account for nonresponse bias across demographic groups.
- To correct for inaccuracies in PCR and serological testing.
Main Methods:
- Utilized Bayesian methods for statistical adjustments.
- Incorporated disease prevalence, test performance data, and census information.
- Applied adjustments for nonresponse and testing errors (false positives/negatives).
Main Results:
- Adjustments significantly impacted unadjusted prevalence estimates.
- Upweighting of data from non-White and Hispanic participants was crucial.
- Consideration of test errors reduced bias in prevalence figures.
Conclusions:
- Bayesian statistical adjustments are vital for accurate disease prevalence estimation.
- Addressing demographic nonresponse and test inaccuracies improves public health surveillance.
- This study provides a robust methodology for future epidemiological investigations.
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