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: 10682401
• 负责人: Anuraag Gopaluni
• 金额: $ 3.37万
• 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/10682401
• 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; 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; Resource-limited setting; Seasons; Sensitivity and Specificity; Series; Signal Transduction; Statistical Methods; Statistical Models; Surveillance Modeling; Symptoms; System; Testing; Time; Time trend; Training; Uncertainty; United States; Work; academic review; analytical tool; associated symptom; career; computerized tools; detection method; disease transmission; emerging pathogen; experience; flexibility; future outbreak; global health; health data; health management; improved; interest; large datasets; mortality; new outbreak; open source; previous outbreak; prospective; response; surveillance strategy; syndromic surveillance; theories; tool; trend; usability

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.

项目概要/摘要 COVID-19 大流行凸显了对强有力的监测和监视系统的需求。 为了对新出现的传染病进行早期发现和应对，必须有强大的分析能力 检查历史和当前数据的工具，以识别关键健康状况中的潜在异常 指标。当缺乏可靠的测试和报告数据时尤其需要这一点。相反，关键 可以跟踪和追踪相关指标，即死亡率和相关疾病的相关症状 分析了。存在两个问题：（1）报告延迟导致当前健康指标数据少计， (2) 先前的异常情况，例如由于过去的疫情爆发导致的死亡率飙升，扭曲了历史或基线数据。 因此，我们的目标是开发方法来进行持续、滚动的监测和疫情检测。 这两个问题的背景。 可以利用合作产生的两个大型数据集：（1）来自 波多黎各疾病控制和预防中心 (CDC) 和公共卫生部 (DPH) 2017 年 1 月至 2021 年 12 月期间，里科、马萨诸塞州和加利福尼亚州，以及 (2) 健康合作伙伴 (PIH) 定期收集与 COVID-19 相关的健康管理信息系统 (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 分析以开发对当前数据进行漏算估算的方法。在此过程中，各种 将解决现有研究中的方法论差距，包括考虑季节性因素 报告滞后模式并提供估计的不确定性度量。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 通过同时解决两个差距来开发滚动疫情检测的方法： 先前的数据畸变和优化关键统计属性，包括偏差、方差和适当的 模型拟合。 这两个目标在传染病监测中是互补的且同等重要。虽然 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。由于与 COVID-19 相关的挑战持续存在并且 新威胁不断出现，用于早期检测的严格统计工具仍然至关重要。只是 同样重要的是，以可访问、易于使用的开源软件包的形式传播这些工具， 拟议研究计划的关键方面。