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: 2032106
• 负责人: Mahesh Banavar
• 金额: $ 10万
• 依托单位: Clarkson 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=2032106&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.

为了在COVID-19的现实下开放经济，需要一套解决方案来尽量减少COVID-19的传播，其中包括为企业提供工具，以尽量减少其员工和客户的风险。重要的是要发现感染者和未感染者之间接触高于平均水平的传播热点。该项目将提供信息，以准确评估COVID-19热点的规模、密度和位置，并根据数据驱动的持续风险评估发布信息充分的公告。将采取每一步措施来确保隐私和网络安全，并将开发特定的算法来确保安全访问和信息传输。该项目将访问疾控中心、约翰霍普金斯和世卫组织的数据库，并创建一个综合网站，在维护隐私的同时，实时发布本地化的COVID-19热点数据。该项目将创建新的算法，并将其嵌入iOS和Android应用程序中，这些应用程序将持续与数据库进行交互。移动的设备以及中央集线器的软件将通过API公开提供给更广泛的社区使用。该项目将使用先进的基于共识的方法，根据最小的收发数据来估计网络中的网络面积/大小、节点位置和节点数量。与现有算法相比，所提出的方法将导致显着的改进。该项目将设计基于共识的算法，以估计（a）中心，半径，因此，网络的大小，和（B）在网络中的用户数量。定位算法将被设计为适用于有噪音和不完整的数据。这项工作与谷歌和苹果使用的联系人追踪技术不同，后者仅限于更新的设备。所提出的算法和软件将在保持与新兴的和现有的移动的技术的兼容性的同时推进最先进的技术。该项目将有助于减少COVID-19感染并拯救生命。该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，自主系统和车队的无基础设施实施，以及位置感知患者护理和其他移动的健康应用。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避难群体的集群，以帮助第一响应者在事件发生后找到幸存者，以及在未来大流行或未来COVID-19浪潮的情况下检测传播节点。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。

项目成果

Distributed Consensus based COVID-19 Hotspot Density Estimation

基于分布式共识的 COVID-19 热点密度估计

• DOI: 10.1109/iisa56318.2022.9904371
• 发表时间: 2022
• 期刊: Systems & Applications (IISA
• 作者: Achalla, Monalisa;Muniraju, Gowtham;Banavar, Mahesh K.;Tepedelenlioglu, Cihan;Spanias, Andreas;Schuckers, Stephanie
• 通讯作者: Schuckers, Stephanie