RAPID: Combining Big Data in Transportation with Hospital Health Data to Build Realistic "Flattening the Curves" Models during the COVID-19 Outbreak

RAPID：将交通大数据与医院健康数据相结合，在 COVID-19 爆发期间构建现实的“压平曲线”模型

基本信息

批准号：
2027678
负责人：
Debbie Niemeier
金额：
$ 8.92万
依托单位：
University of Maryland, College Park
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：
2020-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027678&HistoricalAwards=false
关键词：
RAPID Combining Big Data Transportation

项目摘要

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to improve flattening curve models which can be used to assess and even spatially optimize health care during a rapidly expanding pandemic. This Rapid Response Research (RAPID) project will take advantage of the large-scale availability of location-sensing devices and apps that produce big data on mobility patterns that can be used to better optimize the use of healthcare facilities. This research brings together rapidly unfolding health data with real-time data on mobility. We will examine how these two critical data resources can be linked to better inform policy, identify emerging hotspots, and target critical actions during a pandemic. This research will help public officials to better understand and adapt to changing conditions as a health emergency arises and expands.The spread of the “flattening curves” graphic was significant in promoting public understanding of the criticality of social distancing. These curves, however, were based on simulated data. This research will collect and examine mobility data and public health data to model flattening curves using real data. We combine big data from location-based apps and cellphones with Electronic Medical records from UMMS hospitals, including data on COVID-19 tests, and patient demographics and prognostics. New modeling approaches that quantitatively measure change in collective movement behaviors in response to the fast-evolving COVID-19 outbreak will be linked to hospital usage and capacity. The methods of this research will extend our knowledge of highly integrated systems, like transportation and health, and better prepare the public for future disasters.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.

美国新冠肺炎 (COVID-19) 的爆发为研究人员改进平坦曲线模型提供了重要机会，该模型可用于在快速蔓延的大流行期间评估甚至空间优化医疗保健。该快速响应研究 (RAPID) 项目将利用这一点。位置传感设备和应用程序的大规模可用性，可产生有关移动模式的大数据，可用于更好地优化医疗保健设施的使用。我们将把快速展开的健康数据与实时移动数据结合起来。检查这两个关键的可以将数据资源联系起来，以便更好地为政策提供信息，确定新出现的热点，并在大流行期间采取关键行动。这项研究将帮助政府官员更好地了解和适应突发卫生事件发生和扩大时不断变化的情况。 “曲线”图对于促进公众对社会距离的重要性的理解具有重要意义，但是，这项研究将收集和检查流动性数据和公共卫生数据，以使用真实数据来模拟平坦曲线。大数据来自基于位置的应用程序和手机，包含来自 UMMS 医院的电子病历，包括 COVID-19 测试数据以及患者人口统计和预后数据，通常用于衡量集体运动行为的变化，以应对快速发展的 COVID-19。疫情爆发将与医院的使用和容量联系起来。这项研究的方法将扩展我们对高度集成的系统（如交通和健康）的了解，并更好地为公众应对未来的灾难做好准备。该奖项反映了 NSF 的法定使命，并被认为是值得的。通过评估提供支持基金会的智力价值和更广泛的影响审查标准。