Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Epidemics. 2022 Jun;39:100572. doi: 10.1016/j.epidem.2022.100572. Epub 2022 May 7.
Serosurveys are an important tool to estimate the true extent of the current SARS-CoV-2 pandemic. So far, most serosurvey data have been analyzed with cutoff-based methods, which dichotomize individual measurements into sero-positives or negatives based on a predefined cutoff. However, mixture model methods can gain additional information from the same serosurvey data. Such methods refrain from dichotomizing individual values and instead use the full distribution of the serological measurements from pre-pandemic and COVID-19 controls to estimate the cumulative incidence. This study presents an application of mixture model methods to SARS-CoV-2 serosurvey data from the SEROCoV-POP study from April and May 2020 in Geneva (2766 individuals). Besides estimating the total cumulative incidence in these data (8.1% (95% CI: 6.8%-9.9%)), we applied extended mixture model methods to estimate an indirect indicator of disease severity, which is the fraction of cases with a distribution of antibody levels similar to hospitalized COVID-19 patients. This fraction is 51.2% (95% CI: 15.2%-79.5%) across the full serosurvey, but differs between three age classes: 21.4% (95% CI: 0%-59.6%) for individuals between 5 and 40 years old, 60.2% (95% CI: 21.5%-100%) for individuals between 41 and 65 years old and 100% (95% CI: 20.1%-100%) for individuals between 66 and 90 years old. Additionally, we find a mismatch between the inferred negative distribution of the serosurvey and the validation data of pre-pandemic controls. Overall, this study illustrates that mixture model methods can provide additional insights from serosurvey data.
血清学调查是估计当前 SARS-CoV-2 大流行真实范围的重要工具。到目前为止，大多数血清学调查数据都是使用基于截断值的方法进行分析的，该方法根据预定义的截断值将个体测量值分为血清阳性或阴性。然而，混合模型方法可以从相同的血清学调查数据中获得额外的信息。这些方法避免将个体值二分化，而是使用来自大流行前和 COVID-19 对照的血清学测量的完整分布来估计累积发病率。本研究应用混合模型方法分析了 2020 年 4 月至 5 月在日内瓦进行的 SEROCoV-POP 研究中的 SARS-CoV-2 血清学调查数据（2766 人）。除了估计这些数据中的总累积发病率（8.1%（95%CI：6.8%-9.9%））之外，我们还应用了扩展的混合模型方法来估计疾病严重程度的间接指标，即与住院 COVID-19 患者的抗体水平分布相似的病例比例。该比例在整个血清学调查中为 51.2%（95%CI：15.2%-79.5%），但在三个年龄组之间存在差异：5 至 40 岁人群为 21.4%（95%CI：0%-59.6%），41 至 65 岁人群为 60.2%（95%CI：21.5%-100%），66 至 90 岁人群为 100%（95%CI：20.1%-100%）。此外，我们发现血清学调查的推断阴性分布与大流行前对照的验证数据之间存在不匹配。总体而言，本研究表明，混合模型方法可以从血清学调查数据中提供额外的见解。
