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%].
翻译:估计感染死亡率(IFR)及其与各种利益因素的关系方面的一个关键挑战是确定病例总数。案例总数并不为人所知,因为并非每个人都经过测试,更重要的是,因为接受测试的个人并不代表整个人口。我们提到感染者比未感染者更有可能接受测试的现象,称为“优先测试”。一个未决问题是,在不具体了解数据因优惠测试而偏差的程度的情况下,能否可靠地估计感染死亡率(IFR)。在本文件中,我们采取部分可识别性方法,明确拟订事先经过深思熟虑的假设,并展示一种收集不同样本信息的巴伊西亚模型。当该模型适合欧洲从血清研究和国家官方COVID-19统计数据中获取的数据时,我们估计欧洲的COVID-19 IFR总量为0.53%,95% C.I.=[0.39%,0.69%]。