Casual, Statistical and Mathematical Modeling with Serologic Data

我们将制定方法，加强对新冠肺炎人群的血清学研究的设计和分析，包括未来可能推广的方法，以应对其他季节性疾病(如流感)和新出现的疾病带来的挑战。此外，我们将以创新的方式使用血清学数据来支持能够预测人口水平趋势的数学模型。早期使用方便的人群样本和具有可变且往往不确定的灵敏度和特异度的血清学分析的血清调查因缺乏代表性和对测试特征的考虑不足而受到严厉批评，导致偏见和过度自信(不适当地狭窄的置信限)。目标1将开发有效推断血清阳性率的方法，特别是通过(A)说明有偏抽样，(B)说明不完善的测试，以及(C)开发和测试一种使用血清学测试的滚雪球采样的新方法，以加强疫情检测和接触者追踪。评估血清保护的有效比较--一个人是否受到新冠肺炎感染的免疫反应(特别是抗体)的保护，使其免受再次感染--取决于对混淆的足够控制，这个问题在血清保护研究中以多种方式出现。同样，如果研究没有仔细设计和分析，可能会错误地推断血清保护性的减弱。开发详细的血清学和系统血清学数据集的前所未有的努力提供了新的数据形式，可以利用这些数据更好地为这些推断提供信息。AIM 2将开发一套方法来加强血清保护研究中的因果推断，包括(A)样本大小和能量计算；以及(B)改进对血清学数据的利用，以减少由于混淆和风险补偿而产生的偏差。目标3将开发新的数学建模方法，并应用它们来量化新冠肺炎由于各种形式的风险异质性和混合中的分类而可能导致的群体免疫阈值的降低。AIM 4将开发新冠肺炎传播模型，纳入关于感染、脱落和症状免疫持续时间和性质的新证据，以估计在对免疫进展的不同假设下疾病发病率将如何变化。目标5将开发传播模型，以评估聚集设施(如监狱和养老院)的最佳队列安排，并特别注意这些安排有益所需的免疫力的性质。最后，疫苗供应最初可能是有限的，因此有必要有效地使用它们。目标6将调查血清学数据与其他类型数据相结合的使用，以优化稀缺疫苗的分配。

We will develop methods to enhance the design and analysis of serologic studies of populations with respect to COVID-19, including methods that may be generalized in the future to address challenges raised by other seasonal diseases (such as influenza) and newly emerging diseases. In addition, we will use serologic data in innovative ways to underpin mathematical models that can project population-level trends. Early serosurveys using convenience samples of the population and serologic assays with variable and often uncertain sensitivity and specificity were heavily criticized, for unrepresentativeness and inadequate accounting for test characteristics, resulting in bias and overconfidence (unduly narrow confidence bounds). Aim 1 will develop methods for valid inference of seroprevalence, specifically by (a) accounting for biased sampling, (b) accounting for imperfect tests, and (c) developing and testing a novel approach to snowball sampling employing serologic tests to enhance outbreak detection and contact tracing. Valid comparisons that assess seroprotection—whether, how much, and how long an individual is protected by an immune response to a COVID-19 infection (specifically, by antibodies) against reinfection—rely on adequate control for confounding, an issue that arises in multiple ways specific to seroprotection studies. Likewise, waning of seroprotection may be inferred in error if studies are not carefully designed and analyzed. The unprecedented efforts to develop detailed serologic and systems serologic data sets provide new forms of data that can be leveraged to better inform these inferences. Aim 2 will develop a suite of methods to enhance causal inference in seroprotection studies, including (a) sample size and power calculations; and (b) improved exploitation of serological data to reduce biases due to confounding and risk compensation. Aim 3 will develop new mathematical modeling approaches and apply them to quantify the likely reduction in the herd immunity threshold for COVID-19 due to various forms of risk heterogeneity and assortativeness in mixing. Aim 4 will develop models of COVID-19 transmission that accommodate emerging evidence about the duration and nature of immunity to infection, shedding, and symptoms, to obtain estimates of how illness attack rates will differ under varying assumptions about the progress of immunity. Aim 5 will develop transmission models to assess optimal cohorting arrangements in congregate facilities (eg prisons and nursing homes), with special attention to the nature of immunity required for these arrangements to be beneficial. Finally, vaccine supplies may be initially limited, necessitating efficient use of them. Aim 6 will investigate the use of serologic data in combination with other types of data to optimize allocation of scarce vaccines.