During the COVID-19 pandemic, governments and public health authorities have used seroprevalence studies to estimate the proportion of persons within a given population who have antibodies to SARS-CoV-2. Seroprevalence is crucial for estimating quantities such as the infection fatality ratio, proportion of asymptomatic cases, and differences in infection rates across population subgroups. However, serologic assays are prone to false positives and negatives, and non-probability sampling methods may induce selection bias. In this paper, we consider nonparametric and parametric seroprevalence estimators that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the finite sample performance of the estimators over a range of assay characteristics and sampling scenarios. The methods are used to estimate SARS-CoV-2 seroprevalence in asymptomatic individuals in Belgium and North Carolina.

翻译：在COVID-19大流行期间,政府和公共卫生当局使用血清阳性研究来估计特定人群中具有SARS-COV-2抗体的人的比例。对于估计感染死亡率、无症状病例比例和各人口分组感染率的差异等数量而言,血清反应至关重要,不过,血清分析容易出现假正数和负数,非概率抽样方法可能导致选择偏差。在本文件中,我们考虑采用非参数和参数性血清反应估测器,利用鉴定数据,假设在共同变量定义的阶层内采样的概率相同,从而应对两种挑战。两种估计都显示一致和无症状的正常,并得出前后一致的估计数。进行了模拟研究,比较了对一系列测定特征和抽样假设情景的估量的有限样本性能。我们使用这些方法来估计比利时和北卡罗莱地区个体中SARS-COV-2的血清反应率。