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

为了结合新冠肺炎的现实开放经济，需要一套解决方案来将新冠肺炎的传播降至最低，其中包括为企业提供工具，以将员工和客户面临的风险降至最低。重要的是要发现感染者和非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，以准确评估新冠肺炎热点的规模、密度和位置，并基于数据驱动的持续风险评估发布知情建议。将采取每一步来确保隐私和网络安全，并将为安全访问和信息传输制定具体的算法。该项目将访问美国疾病控制与预防中心、约翰·霍普金斯大学和世界卫生组织的数据库，并创建一个全面的网站，以传播实时的本地化新冠肺炎热点数据，同时保护隐私。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，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区域/大小、节点位置和网络中的节点计数，这些方法基于最少的发送-接收数据。与现有算法相比，所提出的方法将有显著的改进。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。定位算法将被设计成能够处理噪声和不完整的数据。这项拟议的工作不同于谷歌和苹果使用的联系人追踪技术，后者仅限于较新的设备。拟议的算法和软件将提升最先进的技术水平，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新冠肺炎感染，拯救生命。这项研究还将适用于其他领域，如E911系统、室内用户跟踪、适用于机器人、自主系统和车队的免基础设施实施，以及位置感知患者护理和其他移动医疗应用。所开发的算法可用于其他紧急情况，例如在地震和海啸情况下定位避难所集群，以帮助应急人员在事件发生后找到幸存者，以及在未来的流行病或未来的新冠肺炎浪潮的情况下检测传播节点。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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.