论文标题
贝叶斯调整以估算Covid-19感染率的优先测试
Bayesian adjustment for preferential testing in estimating the COVID-19 infection fatality rate
论文作者
论文摘要
估计感染死亡率（IFR）及其与各种感兴趣因素的关系的关键挑战是确定病例总数。案件总数不知道，因为并非每个人都经过测试，但更重要的是，因为经过测试的个体并不代表整个人口。我们将与未感染个体相比，被感染的个体更有可能进行测试的现象是“优先测试”。一个空旷的问题是，是否可以在没有任何特定知识的情况下可靠地估算IFR，以了解数据偏向优先测试的程度。在本文中，我们采用了一种部分可识别性方法，清楚地制定了可以做出故意的先前假设的地方，并提出了贝叶斯模型，该模型汇集了来自不同样本的信息。当该模型适合从血清阳性研究和国家官方COVID-19统计数据中获得的欧洲数据时，我们估计欧洲的整体Covid-19 IFR为0.53％，95％C.I. = [0.39％，0.69％]。
A key challenge in estimating the infection fatality rate (IFR) -- and its relation with various factors of interest -- is determining the total number of cases. The total number of cases is not known because not everyone is tested, but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals are more likely to be tested than non-infected individuals, as "preferential testing." An open question is whether or not it is possible to reliably estimate the IFR without any specific knowledge about the degree to which the data are biased by preferential testing. In this paper we take a partial identifiability approach, formulating clearly where deliberate prior assumptions can be made and presenting a Bayesian model which pools information from different samples. When the model is fit to European data obtained from seroprevalence studies and national official COVID-19 statistics, we estimate the overall COVID-19 IFR for Europe to be 0.53%, 95% C.I. = [0.39%, 0.69%].
