Casual, Statistical and Mathematical Modeling with Serologic Data

使用血清学数据进行休闲、统计和数学建模

• 批准号: 10264480
• 负责人: William Hanage
• 金额: $ 169.51万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264480
• 关键词: 2019-nCoV; Address; Age; Attention; Biological Assay; COVID-19; Caregivers; Cessation of life; Communicable Diseases; Coronavirus; Data; Detection; Dose; Ethnic Origin; Goals; Herd Immunity; Human; Immunity; Immunoglobulin G; Individual; Infection; Infection prevention; Intervention; Lead; Longevity; Malignant Neoplasms; Mathematics; Modeling; Nature; Nursing Homes; Outcome; Persons; Population; Population Heterogeneity; Predisposition; Prevention; Prisons; Public Health; Race; Reporting; Risk Factors; Role; Scientific Advances and Accomplishments; Serology; Serology test; Seroprevalences; Severities; Signal Transduction; Structure; Symptoms; Time; Ursidae Family; Vaccination; Vaccines; Variant; Writing; age group; cohort; comorbidity; improved; infection risk; mathematical model; novel; pandemic disease; response; transmission process

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.

我们将开发方法，以加强针对COVID-19的人群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可以预测人口水平趋势的数学模型。早期的血清学调查使用方便的人口样本和血清学检测，灵敏度和特异性可变，往往不确定，受到了严厉的批评，因为不具有代表性和不充分的考虑测试的特点，导致偏见和过度自信（过度狭窄的置信区间）。目标1将开发有效推断血清阳性率的方法，特别是通过（a）解释有偏倚的采样，（B）解释不完善的测试，以及（c）开发和测试一种新的方法，采用血清学测试来提高爆发检测和接触者追踪。评估血清保护的有效比较-个体是否受到COVID-19感染的免疫应答（特别是抗体）的保护，以及保护了多少和多长时间-依赖于对混淆的充分控制，这是血清保护研究特有的多方面问题。同样，如果研究没有仔细设计和分析，血清保护的减弱可能会被错误地推断出来。开发详细的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，可以利用这些数据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断，包括（a）样本量和功效计算;（B）改进血清学数据的利用，以减少由于混淆和风险补偿而产生的偏倚。目标3将开发新的数学建模方法，并将其应用于量化由于各种形式的风险异质性和混合的不确定性而导致的COVID-19群体免疫阈值的可能降低。目标4将开发COVID-19传播模型，以适应新出现的关于对感染、脱落和症状的免疫力的持续时间和性质的证据，以估计疾病发作率在不同的免疫力进展假设下的差异。目标5将开发传播模型，以评估聚集设施（如监狱和疗养院）中的最佳聚集安排，特别注意这些安排所需的免疫性质。最后，疫苗供应最初可能有限，需要有效利用。 目的6将研究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。