Statistical Methods for Improving Real-Time Public Health Surveillance and Integrated Outbreak Detection

项目总结/摘要 COVID-19大流行凸显了对强有力的监测和监控系统的需求。 为了对新出现的传染病进行早期检测和应对， 检查历史和当前数据的工具，以确定关键健康问题中的潜在偏差 指标当缺乏可靠的测试和报告数据时，这一点尤其需要。相反，Key 可以跟踪相关指标，即死亡率和感兴趣疾病的相关症状， 分析了存在两个问题：（1）报告延迟导致当前健康指标数据的少计， 以及（2）先前的异常，例如由于过去的爆发而导致的死亡率峰值，扭曲了历史或基线数据。 因此，其目标是制定方法，在非洲大陆进行持续的滚动监测和爆发检测。 这两个问题的背景。 两个大的数据集产生的合作是可用的：（1）国家一级的死亡率数据， 疾病控制和预防中心（CDC）和公共卫生部门（DPH）在波多黎各 2017年1月至2021年12月，马萨诸塞州和加州的里科，以及（2）健康合作伙伴（PIH） 常规收集的与COVID-19相关的卫生管理信息系统（HMIS）数据 指标，特别是来自8个国家900个卫生机构的急性呼吸道感染（ARI） 2016年1月至今。通过拟议研究计划的第一个目标，第一个数据集将是 进行分析，以制定估算当前数据中少计数据的方法。在这样做时，各种 将解决现有研究中的方法差距，包括对季节性的解释， 报告滞后模式，并提供估计不确定性的度量。通过第二个目标， 拟议的研究计划，第二个数据集，沿着一个模拟版本，将进行分析， 通过同时解决两个差距，制定滚动疫情检测方法： 先验数据畸变和优化关键统计特性，包括偏倚、方差和适当的 模型拟合 这两个目标在传染病监测中是相辅相成的，同样重要。而 将分析上述具体数据集和指标，开发的方法将 广泛适用于监测任何关键健康指标。随着COVID-19相关挑战持续存在， 尽管出现了新的威胁，但早期发现的严格统计工具仍然至关重要。只是 同样重要的是以可获得、易于使用的开放源码软件包传播这些工具， 这是研究计划中的一个重要方面。

Project Summary/Abstract The COVID-19 pandemic has accentuated the need for strong monitoring and surveillance systems. To conduct early detection and response to emerging infectious diseases, there must be robust analytical tools that examine historical and current data in order to identify potential aberrations in key health indicators. This is especially needed when reliable testing and reporting data is lacking. Instead, key associated indicators, namely mortality and related symptoms to a disease of interest, can be tracked and analyzed. Two problems exist: (1) reporting delays lead to undercounts in current health indicators data, and (2) prior anomalies such as spikes in mortality due to past outbreaks distort historical or baseline data. Thus, the goal is to develop methods to conduct ongoing, rolling surveillance and outbreak detection in the context of these two issues. Two large datasets resulting from collaborations are available: (1) state-level mortality data from the Centers for Disease Control and Prevention (CDC) and Departments of Public Health (DPH) in Puerto Rico, Massachusetts, and California from January 2017-December 2021, and (2) Partners in Health (PIH) routinely collected health management information systems (HMIS) data on COVID-19-associated indicators, specifically acute respiratory infections (ARI) from 900 health facilities in 8 countries from January 2016-current. Through the first aim of the proposed research plan, the first dataset will be analyzed to develop methods for imputing undercounts in current data. In doing so, various methodological gaps in existing research will be addressed, including accounting for seasonality in reporting lag patterns and providing measures of uncertainty around estimates. Through the second aim of the proposed research plan, the second dataset set, along with a simulated version, will be analyzed to develop methods for rolling outbreak detection by simultaneously addressing two gaps: accounting for prior data aberrations and optimizing key statistical properties including bias, variance, and appropriate model fit. Both goals are complementary and equally important in infectious disease surveillance. While the specific datasets and indicators as described above will be analyzed, the developed methods will be broadly applicable to monitoring of any key health indicators. As COVID-19-related challenges persist and new threats emerge, statistically rigorous tools for early detection remain of paramount importance. Just as important is dissemination of these tools in accessible, easily usable open-source software packages, a key aspect of the proposed research plan.