Abstract
COVID-19 testing studies have become a standard approach for estimating prevalence and fatality rates 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) and over-estimation of fatality rates. 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. We show via an example that the bias corrections can provide dramatic reductions in estimation error.
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8 pages, the only changed is in the title