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

RAPID：基于地理空间的深度分析，用于实时缓解和响应美国农村人口的 COVID-19 爆发

• 批准号: 2027891
• 负责人: Chi-Ren Shyu
• 金额: $ 20万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027891&HistoricalAwards=false
• 关键词: RAPID; Geospatially; Enabled; Deep; Analytics

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.

COVID-19在全球的迅速传播正在改变我们应对大流行的方式，并促使学术界、私营企业和政府利用新技术和新方法，尽量减少对人类生活的影响。虽然对城市地区的疫情给予了很大的关注，但援助农村人口的努力却落后了。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，如医院、互联网、911等，而且社会经济地位总体较低。预计2019冠状病毒病对农村地区的影响将是毁灭性的。该项目为三个农村社区提供研究、科学和COVID-19规划。向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，并使农村人口能够做好抵御能力的准备。科学界将受到农村疾病分析和相关资源管理需求评估和跟踪的启发，获得新的计算方法。将通过创建一个交互式仪表板，向包括农村利益攸关方在内的COVID-19规划界提供这一数学和计算工作的实施，其中的地图和摘要将为一线临床医生和/或公共卫生响应者提供特定农村地区的最新报告和背景。该项目将重点放在密苏里州的农村地区，并计划将该框架扩展到周边各州。该项目探讨是否可以利用地理空间和网络分析方面的最新进展，为美国农村提供可扩展的互联卫生生态系统，以应对COVID-19疫情。它还涉及为未来的COVID-19疫情浪潮带来可解释情报所需的新创新。为了回答这些问题，由计算机、地理信息学、流感、病毒学、病理学、急性病护理和远程医疗专家组成的研究团队计划：(1)该团队将首先利用独特的地理空间分析研究知识库（GeoSPARK）大数据框架快速扩展他们之前的工作，其中包括来自人口普查、医疗保健系统以及围绕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，以解决缺乏专门用于COVID-19风险评估、能力调查和地理决策支持的综合数据框架的问题。(2)团队将开发和实施一套受疾病暴发动态启发的地理空间分析方法，如网络分析（例如情景分析——分析资源分配、遏制等中断的敏感性和影响）、热点分析、背景分析、聚类分析等，以定量衡量风险，评估农村差异的多方面问题。受农村地区现场需求和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以外的情况，如人畜共患疾病暴发、洪水和地震。该奖项反映了美国国家科学基金会的法定使命，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。