Casual, Statistical and Mathematical Modeling with Serologic Data

我们将制定方法，加强COVID-19人群血清学研究的设计和分析，包括将来可能推广的方法，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可以预测人口水平趋势的数学模型。早期的血清调查使用方便的人群样本和血清学分析，具有可变和不确定的敏感性和特异性，因为不具有代表性和对测试特征的不充分考虑，导致偏倚和过度自信（过度狭窄的置信范围）而受到严厉批评。目标1将开发有效推断血清流行率的方法，特别是通过(a)考虑有偏差的抽样，(b)考虑不完善的测试，以及(c)开发和测试采用血清学测试的滚雪球抽样的新方法，以加强疫情检测和接触者追踪。评估血清保护的有效比较——个体对COVID-19感染（特别是抗体）的免疫反应是否、在多大程度上以及在多长时间内对再感染进行保护——依赖于对混淆的充分控制，这是血清保护研究特有的多种方式产生的问题。同样，如果不仔细设计和分析研究，可能会错误地推断出血清保护作用的减弱。开发详细血清学和系统血清学数据集的空前努力提供了新形式的数据，可用于更好地为这些推断提供信息。目标2将开发一套方法，以加强血清保护研究中的因果推理，包括(a)样本量和功率计算；(b)改进对血清学数据的利用，以减少因混杂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，并将其应用于量化由于混合中的各种形式的风险异质性和分类性而导致的COVID-19群体免疫阈值可能降低。目标4将开发COVID-19传播模型，以适应有关感染、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会有何不同。目标5将开发传播模型，以评估聚集设施（如监狱和养老院）中的最佳队列安排，特别注意这些安排有益所需的免疫性质。最后，疫苗供应最初可能有限，因此必须有效利用。目标6将调查血清学数据与其他类型数据的结合使用，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.