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: 10341179
• 负责人: ALISON P GALVANI
• 金额: $ 57.5万
• 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/10341179
• 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; Machine Learning; Medical; Methodology; Methods; Mexico; Modeling; Neighborhoods; Pollution; Population; Prevention; Public Health; Pythons; Readiness; Reporting; Research; Resolution; Respiratory Disease; 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; chronic respiratory disease; 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; syndemic; 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来加速呼吸道病毒的检测 在美国城市爆发了前所未有的局部规模的疫情。我们建议通过以下措施， 结合机器学习数据集成方法和疾病传播的空间模型。的 将开发的动态模型将为区分典型的 非典型疾病趋势和优化方法评估数据源的信息量， 通过快速评估不同的输入数据源，实现特定的公共卫生目标。工作 与当地医疗保健和公共卫生领导者一起，我们将把算法转化为用户友好的在线工具 支持准备计划和决策。 我们的研究围绕三个主要目标展开。在目标1中，我们将应用机器学习， 信号处理方法，以建立跟踪国内新出现疫情的最早指标的系统。 美国7个城市我们将评估反映早期和轻微症状的非临床数据以及临床数据 覆盖服务不足的社区以及地理和人口热点地区，以防止病毒的出现。在Aim中 2.我们将开发反映共循环呼吸道病毒流行病学的次城市规模模型， 慢性呼吸道疾病（CRD）可能会加剧病毒感染。我们将推断病毒传播率 和社会环境风险辅因子，将模型拟合到从以下数据中提取的呼吸道疾病数据： 在过去的九年里，有数百万的电子健康记录（EHR）。然后，我们将与临床和 EHR专家将我们的模型转化为首个严重呼吸道病毒疫情检测系统 将EHR数据整合到CRD上。利用机器学习技术，我们将进一步整合其他 监测，环境，行为和互联网预测数据源，以最大限度地提高准确性， 我们的算法的灵敏度、速度和人口覆盖率。在目标3中，我们将开发一个开放获取的 Python工具包，以促进下一代数据集成到疫情监测模型中。 该项目将产生实用的早期预警算法，用于在高风险环境中检测新出现的病毒威胁。 美国几个城市的时空分辨率，阐明了当前监测中的社会地理差距 系统和热点的病毒出现，并提供了一个强大的设计框架外推这些 算法到其他美国城市。