RAPID: CORPUS: An Edge Intelligence-Assisted Multi-Granularity COVID-19 Risk Predication and Update System

RAPID：CORPUS：边缘智能辅助的多粒度 COVID-19 风险预测和更新系统

• 批准号: 2027251
• 负责人: Weisong Shi
• 金额: $ 15万
• 依托单位: Wayne 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: 2020-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027251&HistoricalAwards=false
• 关键词: RAPID; CORPUS; Edge; Intelligence; Assisted

Since the first COVID-19 case was diagnosed and reported at the end of December 2019, there have been more than 1.6 million COVID-19 cases reported, causing more than 100,000 death worldwide as of April 10, 2020. The outbreak of COVID-19 has significantly affected individuals and our society as a whole, and many national and international events have been canceled over COVID-19 fears, including NBA, NCAA events, Mobile World Congress. For the sake of either individual, institutes, or governments, a risk prediction and update system are urgently needed. However, to design and implement such a system, there are several system challenges. First, how to derive the infection risk level from different granularities, i.e., individual-, event-, and institution-levels? Second, how to dynamically update the risk level based on the latest outbreak news? Third, how to preserve user sensitive data while sharing adequate data for risk level calculation? To attack these challenges in this RAPID project, researchers at Wayne State University and Henry Ford Health Systems design and implement CORPUS, an edge intelligence-assisted, multi-granularity COVID-19 Risk Prediction, and Update System, which includes a mobile app running on personal phones, as well as a large-scale distributed protocol behind the app collecting and updating the information. First, CORPUS will build a multi-granularity risk analysis model, from fine-grained personal risk to small clustered meeting risk, to coarse-grained large clustered event risk, and institutional/organization risk. Second, CORPUS employs a data propagation protocol to build and update the risk analysis model. The data that can contribute to CORPUS include spatial data (such as GPS signal), temporal data (such as calendar event), as well as the input from the user (such as meeting with a specific person). Third, CORPUS leverages privacy-preserving algorithms such as node-level feature pooling and anonymous parameter of the model instead of raw user data, to ensure the confidentiality of personal information when multi-granularity models request personal risk information. With the rapid expansion of COVID-19, there is an urgent need for the individual to know their infection risk when traveling to a place in the foreseeable future. CORPUS can meet their needs by leveraging personalized information and edge intelligence. For a group or an organization, CORPUS will provide risk-related information to help them to judge the feasibility of holding a meeting or an event during an outbreak, especially for large-scale international events (such as the Olympics and World Cup). They can also proactively take action based on the risk information provided by CORPUS to reduce the spread of COVID-19. CORPUS will help governments perceive the risk of infection in their jurisdictions, and thus guide infection prevention and control for effective governance.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.

自 2019 年 12 月底诊断和报告第一例 COVID-19 病例以来，截至 2020 年 4 月 10 日，已报告超过 160 万例 COVID-19 病例，导致全球超过 10 万人死亡。 COVID-19 的爆发对个人和整个社会产生了重大影响，许多国内和国际活动因对 COVID-19 的担忧而被取消，包括 NBA、NCAA 活动、世界移动大会。无论是个人、机构还是政府，都迫切需要一个风险预测和更新系统。然而，要设计和实现这样的系统，存在几个系统挑战。首先，如何从个人、事件、机构等不同粒度推导感染风险等级？第二，如何根据最新的疫情消息动态更新风险级别？第三，如何在共享足够数据进行风险等级计算的同时，保留用户敏感数据？为了应对这个 RAPID 项目中的这些挑战，韦恩州立大学和亨利福特健康系统的研究人员设计并实施了 CORPUS，这是一个边缘智能辅助、多粒度的 COVID-19 风险预测和更新系统，其中包括一个运行在个人电话，以及应用程序背后收集和更新信息的大规模分布式协议。首先，CORPUS将构建多粒度的风险分析模型，从细粒度的个人风险到小集群的会议风险，再到粗粒度的大集群事件风险，以及机构/组织风险。其次，CORPUS采用数据传播协议来构建和更新风险分析模型。可以为 CORPUS 做出贡献的数据包括空间数据（例如 GPS 信号）、时间数据（例如日历事件）以及来自用户的输入（例如与特定人的会议）。第三，CORPUS利用节点级特征池、模型匿名参数等隐私保护算法代替原始用户数据，保证多粒度模型请求个人风险信息时个人信息的机密性。随着COVID-19的迅速蔓延，个人迫切需要在可预见的将来前往某个地方旅行时了解自己的感染风险。 CORPUS 可以通过利用个性化信息和边缘智能来满足他们的需求。对于一个团体或组织来说，CORPUS将提供风险相关信息，帮助他们判断疫情期间举办会议或活动的可行性，特别是大型国际活动（如奥运会、世界杯）。他们还可以根据 CORPUS 提供的风险信息主动采取行动，以减少 COVID-19 的传播。 CORPUS 将帮助各国政府了解其管辖范围内的感染风险，从而指导感染预防和控制以实现有效治理。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。

项目成果

SafeCampus: Multimodal-Based Campus-Wide Pandemic Forecasting

SafeCampus：基于多模式的校园范围内的流行病预测

• DOI: 10.1109/mic.2021.3125571
• 发表时间: 2022
• 期刊: IEEE Internet Computing
• 影响因子: 3.2
• 作者: Lu, Sidi;Wu, Baofu;Cong, Xiaoda;Yao, Yongtao;Shi, Weisong
• 通讯作者: Shi, Weisong