A simple correction for COVID-19 sampling bias

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, indivi...

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Bibliographic Details
Published in:arXiv.org 2021-01
Main Authors: Daniel Andrés Díaz-Pachón, J Sunil Rao
Format: Article
Language:English
Subjects:
Bias
Coronaviruses
COVID-19
COVID-19 diagnostic tests
Decision making
Error correction
Fatalities
Public health
Sampling designs
Signs and symptoms
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2007.07426