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.

COVID-19 测试已成为估计流行率的标准方法，然后协助做出公共卫生决策以遏制和减轻疾病的传播。所使用的抽样设计往往存在偏差，因为它们不能反映真实的基础人群。例如，有严重症状的人比没有症状的人更有可能接受检测。这导致对患病率的估计有偏差（太高）。典型的采样后校正并不总是可行的。在这里，我们提出了一种简单的偏差校正方法，该方法源自并改编自荟萃分析研究中对发表偏差的校正。该方法足够通用，可以进行各种定制，使其在实践中更有用。使用已经收集的信息很容易完成实施。通过模拟和两个真实数据集，