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

项目摘要/摘要 我们的跨学科研究团队将开发算法来加快呼吸道病毒的检测 美国城市爆发了史无前例的局部规模的疫情。我们建议通过以下方式推进疫情检测 将机器学习数据集成方法与疾病传播空间模型相结合。这个 将开发的动态模型将提供机械引擎，以区分典型和 非典型疾病趋势及其优化方法评价数据源的信息量 通过快速评估不同的输入数据来源，实现特定的公共卫生目标。劳作 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 支持备灾计划和决策。 我们提出的研究围绕三个主要目标进行。在目标1中，我们将应用机器学习和 用信号处理方法建立系统，跟踪新出现的疫情的最早指标 七个美国城市。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，以应对病毒的涌现。在AIM 2，我们将开发反映共同传播的呼吸道病毒和 可加重病毒感染的慢性呼吸道疾病(CRD)。我们将推断病毒的传播率 和社会环境风险因素，通过将模型与从 在过去的九年里，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第一个针对严重呼吸道病毒的爆发检测系统 这纳入了CRD上的电子健康记录数据。利用机器学习技术，我们将进一步整合其他 监控、环境、行为和互联网预测数据来源，以最大限度地提高准确性， 我们的算法的敏感度、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 帮助将下一代数据集成到疫情监测模型中的Python工具包。 该项目将产生实用的早期预警算法来检测新出现的病毒威胁 几个美国城市的时空分辨率，阐明了当前监视中的社会地理差距 病毒出现的系统和热点，并为推断这些系统和热点提供了可靠的设计框架 算法推广到美国其他城市。

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.