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.

为了根据 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减少员工和客户的风险。检测感染者和未感染者之间的接触高于平均水平的传播热点非常重要。该项目将提供信息来精确评估 COVID-19 热点的规模、密度和位置，并能够根据数据驱动的持续风险评估发布明智的建议。我们将采取一切措施确保隐私和网络安全，并将开发特定算法以确保安全访问和信息传输。该项目将访问 CDC、约翰·霍普金斯大学和世界卫生组织的数据库，并创建一个综合网站来传播实时本地化的 COVID-19 热点数据，同时维护隐私。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交互。用于移动设备和中央集线器的软件将通过 API 公开提供，供更广泛的社区使用。该项目将使用先进的基于共识的方法来估计网络中的网络面积/大小、节点位置和节点数量。最小的发送-接收数据。与现有算法相比，所提出的方法将带来显着的改进。该项目将设计基于共识的算法来估计（a）中心、半径，从而估计网络的大小，以及（b）网络中的用户数量。定位算法将设计用于处理嘈杂和不完整的数据。拟议的工作与谷歌和苹果使用的接触追踪技术不同，后者仅限于较新的设备。所提出的算法和软件将推进最先进的技术，同时保持与新兴和现有移动技术的兼容性。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医疗应用。开发的算法可用于其他紧急情况，例如在发生地震和海啸时定位避难群体集群，协助急救人员在事件发生后寻找幸存者，以及在未来发生流行病或其他疾病时检测传播节点。 COVID-19 的未来浪潮。外展活动将与研究相结合，包括创建用于传播的软件和网络内容。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。