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.

自从2019年12月底诊断并报告了第一个COVID-19案件以来，报告了超过160万个COVID-19案件，截至2020年4月10日，全世界在全球范围内造成超过100,000次死亡。Covid-19的爆发严重影响了个人，整个社会和我们的社会的整个社会，许多国际和国际活动在包括COVID-19的恐惧中取消了NBA，包括COND nba，包括NABA，NACA，包括CONCRENCA，NACA，CANA，CANA，CANCA，包括CONS COND。为了个人，机构或政府，迫切需要风险预测和更新系统。但是，为了设计和实施这样的系统，有一些系统挑战。首先，如何从不同的粒度（即个人，事件和机构级别）中得出感染风险水平？第二，如何根据最新的暴发新闻动态更新风险水平？第三，如何在共享足够的数据进行风险水平计算的同时保留用户敏感数据？为了在这个快速项目中攻击这些挑战，韦恩州立大学的研究人员和亨利·福特卫生系统设计和实施了Corpus，这是一个由智能辅助的，多个跨性别的Covid-19风险预测和更新系统，其中包括在个人电话上运行的移动应用程序，以及应用程序背后的大型分布式协议收集和更新信息。首先，语料库将建立一个多粒性风险分析模型，从细粒度的个人风险到小型聚集会议风险，到粗粒大的大型聚类事件风险以及机构/组织风险。其次，语料库采用数据传播协议来构建和更新风险分析模型。可以促成语料库的数据包括空间数据（例如GPS信号），时间数据（例如日历事件）以及用户的输入（例如与特定人员会面）。第三，语料库利用隐私算法（例如节点级功能池池和模型的匿名参数）而不是原始用户数据，以确保当多范围模型请求个人风险信息时，请确保个人信息的机密性。随着Covid-19的迅速扩展，迫切需要个人在可预见的未来前往某个地方时知道自己的感染风险。语料库可以通过利用个性化信息和优势情报来满足他们的需求。对于一个团体或组织，语料库将提供与风险相关的信息，以帮助他们判断爆发期间举行会议或活动的可行性，尤其是对于大型国际活动（例如奥运会和世界杯）。他们还可以根据语料库提供的风险信息来主动采取行动，以减少Covid-19的传播。语料库将帮助政府认为其管辖区感染风险，从而指导预防和控制有效治理的感染。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。

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