Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial data

使用机械模型、机器学习和多样化地理空间数据加速美国城市的病毒爆发检测

• 批准号: 10113533
• 负责人: ALISON P GALVANI
• 金额: $ 59.6万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-24 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10113533
• 关键词: Absenteeism; Address; African; Age; Algorithm Design; Algorithms; Area; Articulation; Bayesian Method; Behavioral; Caring; Chronic; Chronic Disease; Cities; Climate; Clinical; Clinical Data; Collaborations; Communicable Diseases; Data; Data Sources; Decision Making; Detection; Disease; Disease Outbreaks; Disease Surveillance; Disease model; Ebola; Electronic Health Record; Ensure; Epidemic; Evaluation; Geography; Goals; Health; Healthcare; Home; Human; Individual; Infection; Influenza; Influenza A Virus, H1N1 Subtype; Interdisciplinary Study; International; Internet; Intervention; Location; Lung diseases; Machine Learning; Medical; Methodology; Methods; Mexico; Modeling; Neighborhoods; Pollution; Population; Prevention; Public Health; Pythons; Readiness; Reporting; Research; Resolution; Risk; Rural; Schools; Sentinel; Series; Signal Transduction; Social Environment; Specific qualifier value; Speed; Subgroup; Surveillance Modeling; Symptoms; System; Techniques; Testing; Time; Translating; Uncertainty; Validation; Viral; Virus; Virus Diseases; Visualization; Work; World Health Organization; austin; base; cofactor; comorbidity; dashboard; data acquisition; data handling; data integration; design; detection method; detection platform; digital; disease transmission; diverse data; epidemiologic data; epidemiological model; experimental study; flexibility; global health; health care availability; health goals; high risk; high risk population; influenza outbreak; influenzavirus; innovation; insight; metropolitan; next generation; novel; outcome prediction; pandemic disease; public health intervention; respiratory virus; school district; signal processing; simulation; social media; sociodemographic group; socioeconomics; sound; spatiotemporal; stem; tool; transmission process; trend; underserved community; user-friendly; viral transmission

Project Abstract/Summary Our interdisciplinary research team will develop algorithms to accelerate the detection of respiratory virus outbreaks at an unprecedented local scale in US cities. We propose to advance outbreak detection by combining machine learning data integration methods and spatial models of disease transmission. The dynamic models that will be developed will provide mechanistic engines for distinguishing typical from atypical disease trends and the optimization methods evaluate the informativeness of data sources to achieve specified public health goals through the rapid evaluation of diverse input data sources. Working with local healthcare and public health leaders, we will translate the algorithms into user-friendly online tools to support preparedness plans and decision-making. Our proposed research is organized around three major aims. In Aim 1, we will apply machine learning and signal processing methods to build systems that track the earliest indicators of emerging outbreaks within seven US cities. We will evaluate non-clinical data reflecting early and mild symptoms as well as clinical data covering underserved communities and geographic and demographic hotspots for viral emergence. In Aim 2, we will develop sub-city scale models reflecting the syndemics of co-circulating respiratory viruses and chronic respiratory diseases (CRD) that can exacerbate viral infections. We will infer viral transmission rates and socio-environmental risk cofactors by fitting the model to respiratory disease data extracted from millions of electronic health records (EHRs) for the last nine years. We will then partner with clinical and EHR experts to translate our models into the first outbreak detection system for severe respiratory viruses that incorporates EHR data on CRDs. Using machine learning techniques, we will further integrate other surveillance, environmental, behavioral and internet predictor data sources to maximize the accuracy, sensitivity, speed and population coverage of our algorithms. In Aim 3, we will develop an open-access Python toolkit to facilitate the integration of next generation data into outbreak surveillance models. This project will produce practical early warning algorithms for detecting emerging viral threats at high spatiotemporal resolution in several US cities, elucidate socio-geographic gaps in current surveillance systems and hotspots for viral emergence, and provide a robust design framework for extrapolating these algorithms to other US cities.

项目摘要/摘要 我们的跨学科研究团队将开发算法以加速呼吸道病毒的检测 美国城市前所未有的地方规模爆发。我们建议通过 结合机器学习数据整合方法和疾病传播的空间模型。这 将开发的动态模型将提供机械引擎，以区分典型 非典型疾病趋势和优化方法评估了数据源的信息性 通过快速评估各种输入数据源来实现指定的公共卫生目标。在职的 与当地的医疗保健和公共卫生领导者一起，我们将这些算法转化为用户友好的在线工具 支持准备计划和决策。 我们提出的研究是围绕三个主要目标组织的。在AIM 1中，我们将应用机器学习和 信号处理方法构建系统，以跟踪最早的爆发指标 美国七个城市。我们将评估反映早期和轻度症状以及临床数据的非临床数据 涵盖了服务不足的社区以及病毒出现的地理和人口热点。目标 2，我们将开发反映共同循环呼吸道病毒和共同体的次城量表模型和 慢性呼吸道疾病（CRD）会加剧病毒感染。我们将推断病毒传播率 通过将模型拟合到从中提取的呼吸道疾病数据中，以及社会环境风险辅助因子 在过去的九年中，数百万的电子健康记录（EHR）。然后，我们将与临床合作 EHR专家将我们的模型转化为严重呼吸病毒的第一个爆发检测系统 其中包含了有关CRD的EHR数据。使用机器学习技术，我们将进一步整合其他 监视，环境，行为和互联网预测数据源以最大化准确性， 我们算法的敏感性，速度和人口覆盖范围。在AIM 3中，我们将开发一个开放式访问 Python工具包促进将下一代数据集成到爆发监视模型中。 该项目将产生实用的预警算法，以检测高度的病毒威胁 美国几个城市的时空分辨率，阐明当前监视的社会地理差距 用于病毒出现的系统和热点，并为推断这些框架提供了强大的设计框架 美国其他城市的算法。