Andrei C. Rusu, Katayoun Farrahi, Mahesan Niranjan
{"title":"EpiCURB: Learning to Derive Epidemic Control Policies","authors":"Andrei C. Rusu, Katayoun Farrahi, Mahesan Niranjan","doi":"10.1109/mprv.2023.3329546","DOIUrl":null,"url":null,"abstract":"The effectiveness of an epidemic control policy relies largely on how much effort is invested in every public health measure. Unfortunately, it is seldom possible to optimally allocate funds to these measures if the isolated effect of each intervention cannot be reliably estimated. We show how this challenge can be overcome by utilizing EpiCURB, a simulation-control framework that enables us to measure the effect of both untargeted and prioritized interventions on the epidemic outcome, where the latter are guided by reinforcement learning routines that effectively rank eligible individuals.","PeriodicalId":55021,"journal":{"name":"IEEE Pervasive Computing","volume":"72 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Pervasive Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/mprv.2023.3329546","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The effectiveness of an epidemic control policy relies largely on how much effort is invested in every public health measure. Unfortunately, it is seldom possible to optimally allocate funds to these measures if the isolated effect of each intervention cannot be reliably estimated. We show how this challenge can be overcome by utilizing EpiCURB, a simulation-control framework that enables us to measure the effect of both untargeted and prioritized interventions on the epidemic outcome, where the latter are guided by reinforcement learning routines that effectively rank eligible individuals.
期刊介绍：
IEEE Pervasive Computing explores the role of computing in the physical world–as characterized by visions such as the Internet of Things and Ubiquitous Computing. Designed for researchers, practitioners, and educators, this publication acts as a catalyst for realizing the ideas described by Mark Weiser in 1988. The essence of this vision is the creation of environments saturated with sensing, computing, and wireless communication that gracefully support the needs of individuals and society. Many key building blocks for this vision are now viable commercial technologies: wearable and handheld computers, wireless networking, location sensing, Internet of Things platforms, and so on. However, the vision continues to present deep challenges for experts in areas such as hardware design, sensor networks, mobile systems, human-computer interaction, industrial design, machine learning, data science, and societal issues including privacy and ethics. Through special issues, the magazine explores applications in areas such as assisted living, automotive systems, cognitive assistance, hardware innovations, ICT4D, manufacturing, retail, smart cities, and sustainability. In addition, the magazine accepts peer-reviewed papers of wide interest under a general call, and also features regular columns on hot topics and interviews with luminaries in the field.
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