RAPID: Collaborative Research: Covid-19 Hotspot Network Size and Node Counting using Consensus Estimation

RAPID：协作研究：使用共识估计的 Covid-19 热点网络规模和节点计数

• 批准号: 2032114
• 负责人: Andreas Spanias
• 金额: $ 10万
• 依托单位: Arizona State University
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032114&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Covid; 19

In order to open up the economy in light of the reality of COVID-19, a suite of solutions are needed to minimize the spread of COVID-19 which include providing tools for businesses to minimize the risk for their employees and customers. It is important to detect transmission hotspots where the contact between infected and uninfected persons is higher than average. This project will provide information to assess precisely the size, density and locations of COVID-19 hotspots and enable issuing well-informed advisories based on data-driven continuous risk assessment. Every step will be taken to ensure privacy and network security and specific algorithms will be developed for secure access and information transfer. The project will access databases at CDC, Johns Hopkins and the WHO, and create a comprehensive website to disseminate real-time localized COVID-19 hotspot data, while maintaining privacy. The project will create new algorithms and embed them in iOS and Android apps that will continuously interact with databases. The software for mobile devices as well as central hubs will be made publicly available through APIs for use by the broader community.The project will use advanced consensus-based methods for estimating network area/size, node locations and node counts in a network based on minimal transmit-receive data. The proposed methods will lead to significant improvements compared to existing algorithms. The project will design consensus-based algorithms to estimate (a) the center, radius, and consequently, the size of the network, and (b) the number of users in the network. Localization algorithms will be designed that work with noisy and incomplete data. The proposed work is different from the contact-tracing technology used by Google and Apple which is limited to newer devices. The proposed algorithms and software will advance the state of the art while retaining compatibility with emerging and existing mobile technology. The project will help reduce COVID-19 infections and save lives. The research will also have applicability to other fields such as the E911 system, indoor user tracking, infrastructure-free implementations applicable to robotics, autonomous systems and vehicle fleets, and location-aware patient care and other mobile health applications. The developed algorithms can be used in other emergency situations, such as locating clusters of sheltering groups in the case of earthquakes and tsunamis, to assist first responders in finding survivors after an event, and for detection of transmission nodes in the case of future pandemics or future waves of COVID-19. Outreach activities will be integrated with the research and include the creation of software and web content for dissemination.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.

为了鉴于Covid-19的现实开放经济，需要一套解决方案来最大程度地减少Covid-19的传播，其中包括为企业提供工具，以最大程度地降低员工和客户的风险。检测传播热点很重要，其中感染者和未感染者之间的接触高于平均水平。该项目将提供信息，以精确评估Covid-19 Hotspots的大小，密度和位置，并根据数据驱动的持续风险评估启用良好的咨询。将采取每一步，以确保将开发隐私和网络安全以及特定算法以进行安全访问和信息传输。该项目将访问CDC，Johns Hopkins和WHO的数据库，并创建一个全面的网站，以传播实时局部COVID-19 Hotspot数据，同时保持隐私。该项目将创建新算法并将其嵌入iOS和Android应用中，这些应用程序将与数据库不断交互。移动设备和中央集线器的软件将通过API公开提供，以供更广泛的社区使用。该项目将使用基于高级共识的方法来估计网络/节点位置，节点位置和节点计数，基于最小的传输数据。与现有算法相比，提出的方法将导致显着改善。该项目将设计基于共识的算法来估计（a）中心，半径，因此是网络的大小，以及（b）网络中用户数量。本地化算法将设计与嘈杂和不完整的数据一起使用。拟议的工作不同于Google和Apple使用的接触追踪技术，该技术仅限于新设备。拟议的算法和软件将推动最新技术的状态，同时保留与新兴和现有移动技术的兼容性。该项目将有助于减少19日感染并挽救生命。这项研究还将适用于其他领域，例如E911系统，室内用户跟踪，适用于机器人技术，自动驾驶系统和车队的无基础设施实现以及位置感知的患者护理以及其他移动健康应用程序。开发的算法可用于其他紧急情况，例如在地震和海啸的情况下定位庇护所的群体，以帮助急救人员在事件发生后找到幸存者，并在未来的大语或未来的covid-19次波那样检测传播节点。外展活动将与研究融合，并包括创建软件和Web内容以进行传播。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。