Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.
Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland.
PLoS Comput Biol. 2021 Feb 26;17(2):e1008728. doi: 10.1371/journal.pcbi.1008728. eCollection 2021 Feb.
Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys.
大规模的人群血清学检测对于确定当前 SARS-CoV-2 大流行的真实范围至关重要。血清学检测用于测量针对病原体的抗体反应，并使用预设的截止值将定量检测结果分为阳性和阴性，将其作为过去感染的替代指标。由于目前用于检测过去 SARS-CoV-2 感染的检测方法并不完善，血清学调查中阳性个体的比例是累积发病率的有偏估计，通常需要进行校正以考虑灵敏度和特异性。在这里，我们使用一种称为混合模型方法的推断方法来估计累积发病率，该方法不需要通过将定量检测结果直接纳入统计推断过程来定义截止值。我们证实，混合模型优于基于截止值的方法，从而减少了累积发病率估计的偏差和误差。我们说明了如何使用混合模型来优化具有不完善血清学检测的血清学调查的设计。我们还提供了关于需要多少对照和病例血清来充分量化检测的不确定性以实现累积发病率的可靠估计的指导。最后，我们展示了如何使用该方法来估计具有未知定量检测结果分布的感染类别的累积发病率。这是混合模型方法非常有前途的应用，可以确定难以捉摸的无症状 SARS-CoV-2 感染的比例。本文中使用的推断方法的 R 包已提供。我们的研究提倡使用无截止值的血清学检测，特别是如果它们用于确定表征人群而不是个体的参数。这种方法规避了基于截止值的方法在我们目前面临的 SARS-CoV-2 血清学调查中低累积发病率水平和测试准确性方面的一些缺点。
