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

为了在新冠肺炎疫情的现实下实现经济开放，需要为企业提供工具，将员工和客户的风险降到最低，以最大限度地减少新冠肺炎的传播，从而制定一套解决方案。重要的是要在感染者和未感染者之间的接触高于平均水平的地方发现传播热点。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知情的咨询意见。将采取一切措施确保隐私和网络安全，并将制定安全访问和信息传输的具体算法。该项目将接入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，并创建一个综合网站，在保持隐私的同时，传播实时本地化的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，这些应用程序将不断与数据库交互。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，以最小的收发数据为基础估算网络面积/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、半径以及网络的大小，以及(b)网络中的用户数量。定位算法将被设计用于嘈杂和不完整的数据。这项提议的工作与谷歌和苹果公司使用的接触追踪技术不同，后者仅限于较新的设备。拟议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感染并挽救生命。该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、自主系统和车队的无基础设施实施、位置感知患者护理和其他移动健康应用。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人群群，协助急救人员在事件发生后寻找幸存者，以及在未来发生大流行或未来的COVID-19浪潮时检测传播节点。外联活动将与研究结合起来，包括制作软件和网络内容以供传播。该奖项反映了美国国家科学基金会的法定使命，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。

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.