论文标题
在估计和减轻偏见的估计中，案件死亡率估计
On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate
论文作者
论文摘要
群体和国家之间的相对病例死亡率（CFR）是相对风险的关键衡量标准，指导有关持续的Covid-19-19大流行期间稀缺医疗资源分配的政策决策。当监视数据是信息的主要来源时，在活动爆发的中间，估计这些数量涉及补偿时间序列的死亡，病例和恢复时间的竞争偏见。这些包括对病例的时间和严重性依赖性报告以及观察到的患者预后的时间滞后。在COVID-19 CFR估计的背景下，我们调查了这种偏见及其潜在意义。此外，我们从理论上分析了某些偏见的影响，例如致命病例的优先报告对CFR的天真估计量。我们提供了对这些天真估计值的部分校正估计器，这些估计是时间滞后和对死亡和恢复的不完善报告。我们表明，通过测试传染病患者的接触，无论症状是否存在，都会通过限制诊断与死亡之间的协方差来减轻偏见。在https://github.com/aangelopoulos/cfr-covid-19上，我们的分析补充了理论和数值结果以及简单而快速的开源代码库。
The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and recoveries. These include time- and severity- dependent reporting of cases as well as time lags in observed patient outcomes. In the context of COVID-19 CFR estimation, we survey such biases and their potential significance. Further, we analyze theoretically the effect of certain biases, like preferential reporting of fatal cases, on naive estimators of CFR. We provide a partially corrected estimator of these naive estimates that accounts for time lag and imperfect reporting of deaths and recoveries. We show that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death. Our analysis is supplemented by theoretical and numerical results and a simple and fast open-source codebase at https://github.com/aangelopoulos/cfr-covid-19 .
