论文标题
在抗体水平中考虑时间依赖性的患病率估计和最佳分类方法
Prevalence Estimation and Optimal Classification Methods to Account for Time Dependence in Antibody Levels
论文作者
论文摘要
血清学测试可以通过量化提供重要公共卫生指导的感染者的免疫反应来识别过去的感染。单个免疫反应是时间依赖性的，这反映在抗体测量中。此外，随着疾病进展，由于患病率的流行而获得特定测量的可能性。考虑到这些个人和人群级别的影响，我们开发了一个数学模型，该模型提出了一种自然的自适应方案，用于估计流行率作为时间的函数。然后，我们将估计的患病率与最佳决策理论相结合，以开发一种依赖时间的概率分类方案，以最大程度地减少误差。我们通过使用现实世界和合成SARS-COV-2数据的组合来验证该分析，并讨论在现实世界中执行该方案所需的纵向研究的类型。
Serology testing can identify past infection by quantifying the immune response of an infected individual providing important public health guidance. Individual immune responses are time-dependent, which is reflected in antibody measurements. Moreover, the probability of obtaining a particular measurement changes due to prevalence as the disease progresses. Taking into account these personal and population-level effects, we develop a mathematical model that suggests a natural adaptive scheme for estimating prevalence as a function of time. We then combine the estimated prevalence with optimal decision theory to develop a time-dependent probabilistic classification scheme that minimizes error. We validate this analysis by using a combination of real-world and synthetic SARS-CoV-2 data and discuss the type of longitudinal studies needed to execute this scheme in real-world settings.
