使用辅助信息解决COVID-19病例计数数据中的选择偏差和测量误差
Addressing selection bias and measurement error in COVID-19 case count data using auxiliary information
摘要：
冠状病毒病例数数据影响了政府政策，并推动了大多数流行病学预测。有限的检测被认为是导致COVID-19大流行信息极少的主要原因。虽然扩大检测值得称赞，但测量误差和选择偏差是限制我们对COVID-19大流行的理解的两个最大问题;增加检测能力无法完全解决这两个问题。在本文中，我们证明了它们对估计点患病率和有效繁殖数的影响。我们表明，在美国的数百万分子测试的基础上的估计具有相同的均方误差作为一个小的简单的随机样本。为了解决这个问题，一个程序，结合病例计数数据和随机样本随着时间的推移，估计选择倾向的基础上的关键协变量信息。然后，我们将这些选择倾向与流行病学预测模型相结合，构建了一个\emph{doubly robust}估计方法，同时考虑测量误差和选择偏差。然后，使用病例计数，住院和死亡数据以及人口统计信息，从4月25日至29日收集的全州随机分子样本以及德尔福的COVID-19趋势和影响调查，将该方法应用于估计印第安纳州的活动感染率。最后，我们根据所提出的方法提出了一系列建议。
英文摘要：
Coronavirus case-count data has influenced government policies and drives most epidemiological forecasts. Limited testing is cited as the key driver behind minimal information on the COVID-19 pandemic. While expanded testing is laudable, measurement error and selection bias are the two greatest problems limiting our understanding of the COVID-19 pandemic; neither can be fully addressed by increased testing capacity. In this paper, we demonstrate their impact on estimation of point prevalence and the effective reproduction number. We show that estimates based on the millions of molecular tests in the US has the same mean square error as a small simple random sample. To address this, a procedure is presented that combines case-count data and random samples over time to estimate selection propensities based on key covariate information. We then combine these selection propensities with epidemiological forecast models to construct a \emph{doubly robust} estimation method that accounts for both measurement-error and selection bias. This method is then applied to estimate Indiana's active infection prevalence using case-count, hospitalization, and death data with demographic information, a statewide random molecular sample collected from April 25--29th, and Delphi's COVID-19 Trends and Impact Survey. We end with a series of recommendations based on the proposed methodology.
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