The purpose of this research is to meet the urgent needs of emergent biological hazard event simulation prediction research due to the new worldwide situation of complex biological hazard. With the help of the Cesium framework, an open source software tool for 3D geographic information technology from WebGL, this paper defines the overall framework of emergent biological hazard event platform. On this basis, a database of emergent biological hazard events is established, and for modules, namely analogue simulation function module, hazard assessment function module, auxiliary decision function module, and intervention measures function module are developed for three application scenarioes of bioterrorism, biological invasion, and infectious diseases. So a 3D earth biological platform for emergent biological hazard events has been implemented, which aims to provide a visual analysis platform for emergent biological hazard event research decision-making with data support and software platform guarantee.
[1] 江东. 地理信息技术提升突发生物危害事件评估决策能力[J]. 地球信息科学学报, 2016, 18(12):1725. Jiang Dong. Geographic Information Technology (GIS) enhances the ability to assess and make decisions about sudden biohazard events[J]. Geo-Information Science, 2016, 18(12):1725.
[2] United States General Accounting Office. Bioterrorism:Informa-tion technology strategy could strengthen federal agencies' abili-ties to respond to public health emergencies[R]. Washington D C:General Accounting Office, 2003.
[3] Ryan J R. Consequence management and a model program[M]. Amsterdam:Elsevier, 2016:323-343.
[4] Carley K M, Fridsma D B, Casman E, et al. BioWar:Scalable agent-based model of bioattacks[J]. IEEE Transactions on Sys-tems, Man, and Cybernetics-Part A:Systems and Humans, 2006, 36(2):252-265.
[5] Lee B Y, Bedford V, Roberts M S, et al. Virtual epidemic in a virtual city:Simulating the spread of influenza in a US metro-politan area[J]. Translational Research, 2008, 151(6):275-287.
[6] Del V S Y, Stroud P D, Smith J P, et al. EpiSimS:Epidemic simulation system[R]. New Mexico:Los Alamos National Labo-ratory, 2006.
[7] Barrett C L, Beckman R J, Berkbigler K P, et al. Transims:Transportation analysis simulation[R]. New Mexico:Los Ala-mos National Laboratory, 2000.
[8] Chao D L, Halloran M E, Obenchain V J, et al. Flu TE, a pub-licly available stochastic influenza epidemic simulation model[J]. PLoS Computational Biology, 2010, 6(1):e1000656.
[9] Barrett C L, Eubank S G, Smith J P, et al. If smallpox strikes Portland[J]. Scientific American, 2005, 292(3):54-61.
[10] MIDAS. Download FluTE[EB/OL]. (2014-11-09)[2015-09-20]. http://betaweb.rods.pitt.edu/digital-commons/main.
[11] 祖正虎, 许晴, 张文斗, 等. 突发传染病大规模传播模拟系统简析[J]. 军事医学, 2012, 36(10):788-792. Zu Zhenghu, Xu Qing, Zhang Wendou, et al. Analysis on large-scale transmission simulation systems of emerging infectious diseases[J]. Military Medical Sciences, 2012, 36(10):788-792.
[12] 黎彬, 许增禄, 张虎林, 等. 世界主要国家突发传染病应对系统对比分析[J]. 医学信息学杂志, 2005, 26(6):404-409. Li Bin, Xu Zenglu, Zhang Linhu, et al. Comparative analysis of coping system of epidemics in major countries of the world[J]. Journal of Medical Informatics, 2005, 26(6):404-409.
[13] 朱联辉, 郑涛, 赵达生. 美国反生物恐怖信息系统建设及启示[J]. 解放军预防医学杂志, 2007(4):309-311. Zhu Lianhui, Zheng Tao, Zhao Dasheng. American antibiotics information system construction and enlightenment[J]. Journal of Preventive Medicine of Chinese People's Liberation Army, 2007(4):309-311.
[14] 李俊金. 基于Cordova和Cesium的移动3D WebGIS系统实现[J]. 电子技术与软件工程, 2017(8):55-57. Li Junjin. Implementation of mobile 3D WebGIS based on cordova and cesium[J]. Electronic Technology & Software Engineering[J]. 2017(8):55-57.
[15] 朱栩逸, 苗放. 基于Cesium的三维WebGIS研究及开发[J]. 科技创新导报, 2015, 12(34):9-11. Zhu Xuyi, Miao Fang. The research and development of three-dimensional GIS based on cesium[J]. Science and Technology Innovation Herald, 2015, 12(34):9-11.
[16] 刘纪远, 钟耳顺, 庄大方, 等. SARS控制与预警地理信息系统的研制与应用[J]. 遥感学报, 2003(5):337-344. Liu Jiyuan, Zhong Ershun, Zhuang Dafang, et al. Development and application of national SARS disease controlling and pre-warning information system[J]. Journal of Remote Sensing, 2003(5):337-344.
[17] 曹务春, 方立群, 徐友富, 等. 一种突发生物事件现场危害评估模拟系统:CN101894353A[P]. 2010-11-24. Cao Wuchun, Fang Liqun, Xu Youfu, et al. A Simulation system for field hazard assessment of biological incidents:CN101894353A[P]. 2010-11-24.
[18] 罗军, 章昉, 李超. 面向微博数据的流感疫情监测分析方法及系统:CN103593462A[P]. 2014-02-19. Luo Jun, Zhang Fang, Li Chao. Method and system for monitoring and analyzing flu epidemic based on Weibo data:CN103593462A[P]. 2014-02-19.
[19] 万方浩, 张润志, 王福祥, 等. 主要农业入侵生物的预警与监控技术[J]. 中国科技成果, 2013(24):70-71. Wan Fanghao, Zhang Runzhi, Wang Fuxiang, et al. Early warning and monitoring technologies of major agricultural invasive organisms[J]. China Science and Technology Achievements. 2013(24):70-71.
[20] 李春林, 刘淼, 胡远满, 等. 基于增强回归树和Logistic回归的城市扩展驱动力分析[J]. 生态学报, 2014, 34(3):727-737. Li Chunlin, Liu Miao, Hu Yuanman, et al. Driving forces analysis of urban expansion based on boosted regression trees and Logistic regression[J]. Acta Ecologica Sinica. 2014, 34(3):727-737.
[21] 尹才, 刘淼, 孙凤云, 等. 基于增强回归树的流域非点源污染影响因子分析[J]. 应用生态学报, 2016, 27(3):911-919. Yin Cai, Liu Miao, Sun Fengyun, et al. Influencing factors of non-point source pollution of watershed based on boosted regression tree algorithm[J]. Chinese Journal of Applied Ecology. 2016, 27(3):911-919.
[22] Glenn De'ath. Boosted trees for ecological modeling and pre-diction[J]. Ecology, 2007, 88(1):243-251.
