A simple correction for COVID-19 sampling bias

TL;DR

This paper introduces a simple, adaptable bias correction method for COVID-19 prevalence estimates, addressing sampling bias issues in testing data to improve accuracy in public health decision-making.

Contribution

It presents a novel bias correction approach adapted from meta-analysis publication bias correction, applicable with existing data and customizable for various sampling scenarios.

Findings

Bias correction significantly reduces estimation error in simulations.

Method effectively applied to real COVID-19 datasets.

Provides a practical tool for improving prevalence estimates.

Abstract

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.