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

改进实时公共卫生监测和综合疫情检测的统计方法

• 批准号: 10535624
• 负责人: Anuraag Gopaluni
• 金额: $ 3.87万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10535624
• 关键词: Accounting; Acute respiratory infection; Address; Africa; Algorithms; Area; COVID-19; COVID-19 pandemic; California; Centers for Disease Control and Prevention (U.S.); Cessation of life; Collaborations; Communicable Diseases; Computer software; Country; Data; Data Reporting; Data Set; Detection; Development; Disease; Disease Outbreaks; Disease Surveillance; Early Diagnosis; Ebola; Emerging Communicable Diseases; Event; Future; Goals; Health; Health care facility; Infectious Disease Epidemiology; Journals; Latin America; Lead; Liberia; Management Information Systems; Massachusetts; Measures; Methodology; Methods; Modeling; Monitor; Pattern; Peer Review; Population Surveillance; Procedures; Property; Public Health; Publishing; Puerto Rico; Reporting; Research; Research Personnel; Research Training; Resource-limited setting; Sensitivity and Specificity; Series; Signal Transduction; Statistical Methods; Statistical Models; Surveillance Modeling; Symptoms; System; Testing; Time; Time trend; Uncertainty; United States; Work; academic review; analytical tool; associated symptom; base; career; computerized tools; detection method; disease transmission; emerging pathogen; experience; flexibility; global health; health data; health management; improved; interest; large datasets; mortality; open source; prospective; response; surveillance strategy; syndromic surveillance; theories; tool; trend

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.

项目摘要/摘要 COVID-19大流行已经加强了对强大监测和监视系统的需求。 要进行早期检测和对新兴传染病的反应，必须进行鲁棒的分析 检查历史和当前数据以确定关键健康的潜在畸变的工具 指标。当缺乏可靠的测试和报告数据时，这尤其需要。而是键 可以跟踪相关指标，即死亡率和相关症状，可以跟踪 分析。存在两个问题：（1）报告延迟导致当前健康指标数据中的欠算， （2）先前的异常情况，例如由于过去爆发而导致的死亡率峰值扭曲了历史或基线数据。 因此，目的是开发进行持续，滚动监视和爆发检测的方法 这两个问题的背景。 可以提供两个由协作产生的大数据集：（1）来自该州的州级死亡率数据 疾病控制与预防中心（CDC）和波多黎各公共卫生部门（DPH） Rico，马萨诸塞州和加利福尼亚从2017年1月至12月至2021年，（2）健康合作伙伴（PIH） 关于COVID-19的常规收集的健康管理信息系统（HMI）数据 指标，特别是来自8个国家的900个医疗机构的急性呼吸道感染（ARI） 2016年1月。通过拟议的研究计划的第一个目的，第一个数据集将是 经过分析以开发当前数据中裁定企业的方法。这样，各种各样 将解决现有研究中的方法论差距，包括考虑季节性 报告滞后模式并提供围绕估计值的不确定性度量。通过第二个目标 拟议的研究计划，第二个数据集以及模拟版本将被分析为 通过同时解决两个空白来开发用于滚动爆发检测的方法： 先前的数据畸变并优化关键统计属性，包括偏差，差异和适当的 模型拟合。 这两个目标在传染病监测中都是互补的，并且同样重要。而 将分析上述特定数据集和指标，开发的方法将是 广泛适用于监视任何关键健康指标。随着共同-19相关的挑战持续存在， 出现了新的威胁，统计上严格的早期检测工具仍然至关重要。只是 重要的是将这些工具传播到可访问的，易于可用的开源软件包中 拟议研究计划的关键方面。