COVID-19抽样偏差的简单校正
A simple correction for COVID-19 sampling bias
摘要：
COVID-19检测已成为估计流行率的标准方法，从而有助于公共卫生决策，以遏制和减缓疾病的传播。所使用的抽样设计往往是有偏见的，因为它们不能反映真实的基本人口。例如，有强烈症状的人比没有症状的人更有可能接受测试。这导致对流行率的估计有偏差（过高）。典型的采样后校正并不总是可能的。在这里，我们提出了一个简单的偏差校正方法，并从Meta分析研究中的发表偏倚校正改编而来。该方法足够通用，允许各种定制，使其在实践中更有用。使用已经收集的信息很容易实现。通过模拟和两个真实的数据集，我们表明，偏差校正可以提供显着的减少估计误差。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
