RAPID: Geospatially-Enabled Deep Analytics for Real-time Mitigation and Response to COVID-19 Outbreak for American Rural Populations

COVID-19在全球的快速传播正在改变我们应对流行病的方式，并促使学术界、私营企业和政府利用新技术和方法来最大限度地减少对人类生活的影响。虽然人们对城市地区的疫情给予了很大关注，但援助农村人口的努力却滞后。农村社区约占美国人口的19.3%，占美国土地面积的95%。他们特别容易受到疾病爆发的影响，因为他们缺乏必要的资源，例如医院、互联网、911以及整体较低的社会经济地位。COVID-19对农村地区的影响预计将是毁灭性的。该项目为三个农村社区提供研究、科学和COVID-19规划。为研究界提供的成果是访问数百层综合地理空间数据，这些数据可用于高级查询和结果可视化，以支持他们自己的COVID-19研究。此外，研究结果将加强对疾病传播行为的了解，并为农村人口的复原力做好准备。科学界将收到受农村疾病分析和相关资源管理需求评估和跟踪启发的新计算方法。这项数学和计算工作的实施将通过创建交互式仪表板提供给COVID-19规划界，包括农村利益相关者，其中地图和摘要将为前线临床医生和/或公共卫生响应者提供最新报告和特定农村地区的背景。该项目的重点是密苏里州的农村地区，并计划将该框架扩展到周边各州。该项目旨在探讨是否可以利用地理空间和网络分析的最新进展，为美国农村地区提供可扩展的互联健康生态系统，以应对COVID-19的爆发。它还解决了为未来COVID-19爆发带来可解释情报所需的新创新。为了回答这些问题，由计算，地理信息学，流感，病毒学，病理学，急性护理和远程医疗专家组成的研究小组计划如下：（1）该团队将首先利用独特的GeoSPatial分析研究知识库快速扩展其先前的工作（GeoSPARK）大数据框架，包括来自人口普查、医疗保健系统的相关数据，以及围绕COVID-19疾病动态的不断变化的信息。GeoSPARK将使用跨多分辨率位置信息的高级复杂查询提供实时分析，以解决缺乏专用于COVID-19风险评估、容量调查和地理决策支持的综合数据框架的问题。(2)该团队将开发和实施一套地理空间分析方法，这些方法受到疾病爆发动态的启发，例如网络分析（例如，情景分析-分析资源分配、遏制等方面中断的敏感性和影响），热点分析、上下文分析、聚类分析等，定量衡量风险，评估农村差距的多方面问题。受该领域需求和农村地区疾病动态启发的分析工具和仪表板具有变革性，将有助于更好地了解COVID-19以外的情景，如人畜共患病爆发、洪水和地震。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The rapid global spread of COVID-19 is changing the way we handle pandemics and has prompted academia, private industry, and government to leverage new technologies and approaches to minimize the impact on human lives. While much attention is on outbreaks in urban areas, the efforts to assist rural populations has lagged. The rural communities encompass roughly 19.3% of the US population and 95% of the US land area. They are especially vulnerable to disease outbreaks due to lower levels of necessary resources, such as access to hospitals, internet, 911, as well as overall lower socioeconomic status. The impacts of COVID-19 in rural areas are expected to be devastating. This project delivers research, scientific, and COVID-19 planning to three rural communities. The deliverable to the research community is access to hundreds of layers of integrated geospatial data that are available for advanced queries and visualization of results to support their own COVID-19 research. In addition, research results will enhance the understanding of disease transmission behavior and enable preparation for resilience in rural populations. The scientific community will receive new computational methods inspired by the rural disease analysis and associated resource management need assessment and tracking. The implementation of this mathematical and computational work will be made available to the COVID-19 planning community, including rural stakeholders, by creation of an interactive dashboard where maps and summaries will provide the frontline clinicians and/or public health responders up-to-date reports and context for specific rural areas. The project focuses on Missouri’s rural areas with a plan to extend the framework to the bordering states. This project addresses whether the recent advancements in geospatial and network analyses can be leveraged to provide a scalable connected health ecosystem for rural America in response to the COVID-19 outbreak. It also address the new innovations necessary to bring explainable intelligence the future waves of COVID-19 outbreak. To answer these issues, the research team, consisting of experts in computing, geoinformatics, influenza, virology, pathology, acute care, and telemedicine, plans the following: (1) the team will first rapidly extend their previous work with the unique GeoSPatial Analytical Research Knowledgebase (GeoSPARK) big data framework with relevant data from the Census, healthcare systems, as well as the evolving information surrounding COVID-19 disease dynamics. GeoSPARK will provide real-time analysis using advanced complex queries across multi-resolution locational information to address the lack of an integrated data framework dedicated to COVID-19 risk assessment, capacity investigation, and geo-enabled decision support. (2) The team will develop and implement a suite of geospatial analytic methods which are inspired by the dynamics of disease outbreaks, such as network analysis (e.g., scenario analyses – analyze the sensitivity and impact of disruptions in resource distribution, containment, etc.), hot spot analysis, contextual analysis, clustering analysis, etc., to quantitatively weigh risk and assess the multi-faceted problem of rural disparity. The analytical tools and dashboards inspired by the field’s needs and disease dynamics in rural areas are transformative and will enable better understanding of scenarios other than COVID-19, such as zoonotic disease outbreaks, flooding, and earthquakes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.