  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.
