{"title":"大规模配药、应急准备和生物监测的交互式网络决策支持系统","authors":"Eva K. Lee, F. Pietz, Chien-Hung Chen, Yifan Liu","doi":"10.1145/3079452.3079473","DOIUrl":null,"url":null,"abstract":"In this study, we present an interactive web-based real-time decision support suite, RealOpt©. The system integrates visualization, information and cognitive analytics, and dynamic large-scale computational modeling and optimization tools that allow public health emergency preparedness coordinators to determine optimal response facilities and locations, resource needs and supply-routes, and population flow in real time. With an eye towards flexibility and future system expansion, RealOpt is designed in modular format allowing direct linkage to multiple functional modules. Currently, the system has twelve modules covering emergency response preparedness and operations for biological, chemical, radiological/nuclear incidents, biosurveillance, epidemiology, and decontamination models, operations logistics and networks, a real-time crowd sourcing data feed, and evacuation planning. RealOpt has been used for biodefense and H1N1 regional planning and operations, regional flood and hurricane responses, 2010 Haiti earthquake disaster relief, 2011 Japan Fukushima disaster, 2014-2015 Ebola containment assistance and after-event public health preparedness training in West Africa, and current Zika virus containment analysis. The fast solution engines enable real-time use for rapid decision and scenario analysis, since it requires only one CPU minute to determine an optimal network of facilities and resource needs to serve a population of over 10 million.","PeriodicalId":245682,"journal":{"name":"Proceedings of the 2017 International Conference on Digital Health","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An Interactive Web-based Decision Support System for Mass Dispensing, Emergency Preparedness, and Biosurveillance\",\"authors\":\"Eva K. Lee, F. Pietz, Chien-Hung Chen, Yifan Liu\",\"doi\":\"10.1145/3079452.3079473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we present an interactive web-based real-time decision support suite, RealOpt©. The system integrates visualization, information and cognitive analytics, and dynamic large-scale computational modeling and optimization tools that allow public health emergency preparedness coordinators to determine optimal response facilities and locations, resource needs and supply-routes, and population flow in real time. With an eye towards flexibility and future system expansion, RealOpt is designed in modular format allowing direct linkage to multiple functional modules. Currently, the system has twelve modules covering emergency response preparedness and operations for biological, chemical, radiological/nuclear incidents, biosurveillance, epidemiology, and decontamination models, operations logistics and networks, a real-time crowd sourcing data feed, and evacuation planning. RealOpt has been used for biodefense and H1N1 regional planning and operations, regional flood and hurricane responses, 2010 Haiti earthquake disaster relief, 2011 Japan Fukushima disaster, 2014-2015 Ebola containment assistance and after-event public health preparedness training in West Africa, and current Zika virus containment analysis. The fast solution engines enable real-time use for rapid decision and scenario analysis, since it requires only one CPU minute to determine an optimal network of facilities and resource needs to serve a population of over 10 million.\",\"PeriodicalId\":245682,\"journal\":{\"name\":\"Proceedings of the 2017 International Conference on Digital Health\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 International Conference on Digital Health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3079452.3079473\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 International Conference on Digital Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3079452.3079473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
An Interactive Web-based Decision Support System for Mass Dispensing, Emergency Preparedness, and Biosurveillance
In this study, we present an interactive web-based real-time decision support suite, RealOpt©. The system integrates visualization, information and cognitive analytics, and dynamic large-scale computational modeling and optimization tools that allow public health emergency preparedness coordinators to determine optimal response facilities and locations, resource needs and supply-routes, and population flow in real time. With an eye towards flexibility and future system expansion, RealOpt is designed in modular format allowing direct linkage to multiple functional modules. Currently, the system has twelve modules covering emergency response preparedness and operations for biological, chemical, radiological/nuclear incidents, biosurveillance, epidemiology, and decontamination models, operations logistics and networks, a real-time crowd sourcing data feed, and evacuation planning. RealOpt has been used for biodefense and H1N1 regional planning and operations, regional flood and hurricane responses, 2010 Haiti earthquake disaster relief, 2011 Japan Fukushima disaster, 2014-2015 Ebola containment assistance and after-event public health preparedness training in West Africa, and current Zika virus containment analysis. The fast solution engines enable real-time use for rapid decision and scenario analysis, since it requires only one CPU minute to determine an optimal network of facilities and resource needs to serve a population of over 10 million.