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Journal of library and information science in agriculture ›› 2020, Vol. 32 ›› Issue (4): 15-22.doi: 10.13998/j.cnki.issn1002-1248.2020.03.16-0163

• Epidemic prevention • Previous Articles     Next Articles

Multi-dimensional intelligence model for public health emergencies and its application

MU Di1,2, CHEN An1,2   

  1. 1. Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190;
    2. University of Chinese Academy of Sciences, Beijing 100049
  • Received:2020-03-16 Online:2020-04-05 Published:2020-04-28

Abstract: [Purpose/Significance] The public health emergencies need to be analyzed scientifically and reasonably. Their details and in-depth information are very important for decision-making. [Method/Process] Based on the DIIS theory and methodology in think tanks, this paper constructs a multi-dimensional intelligence model for public health emergencies. The model is further used in the analysis of the outbreak of the novel coronavirus pneumonia. [Results/Conclusions] The multi-dimensional intelligence model of public health events includes five aspects, namely subject, object, medium, process and field. In the model, multi-subject, multi-item, multi-attribute, multi-factor, multi-method, multi-matter, multi-layer, multi-stage, multi-aspect and multi-scale are correlative and interactive. In the outbreak of the novel coronavirus pneumonia, the subjects of emergency management took corresponding actions according to their social responsibilities. Think tanks provide special scientific support for dealing with the public health emergency. The outbreak of novel coronavirus pneumonia has a negative effect on the development of society and economy, but it also created an opportunity for the modernization and intellectualization.

Key words: emergency management, DIIS, intelligence, public health emergencies, novel coronavirus pneumonia

CLC Number: 

  • C931
[1] 突发公共卫生事件应急条例[EB/OL].[2020-02-12].http://www.gov.cn/gongbao/content/2011/content_1860801.htm.
[2] 国家突发公共卫生事件应急预案[EB/OL].[2020-02-12].http://www.gov.cn/yjgl/2006-02/26/content_211654.htm.
[3] 张帆.传染病疫情防控应尽快纳入城市综合防灾减灾规划——应对2020新型冠状病毒肺炎突发事件笔谈会[J/OL].城市规划:1.[2020-03-30].http://kns.cnki.net/kcms/detail/11.2378.TU.20200211.1757.008.html.
[4] 严阅,陈瑜,刘可伋,等.基于一类时滞动力学系统对新型冠状病毒肺炎疫情的建模和预测[J].中国科学:数学,2020,50(03):385-392.
[5] 李志慧,李芊璘,王子晨,等.基于“One Health”理念的新型冠状病毒肺炎防控策略[J].暨南大学学报(自然科学与医学版),2020,41(2),1-6.
[6] 王琛,王旋.新型冠状病毒感染的流行、医院感染及心理预防[J].全科护理,2020,18(03):309-310.
[7] 叶斌,罗海明.城市规划应对特大城市公共卫生事件的几点体会——应对2020新型冠状病毒肺炎突发事件笔谈会[J/OL].城市规划:1-2.[2020-03-30].http://kns.cnki.net/kcms/detail/11.2378.TU.20200212.1135.004.html.
[8] 杨保军.突发公共卫生事件引发的规划思考——应对2020新型冠状病毒肺炎突发事件笔谈会[J/OL].城市规划:1.[2020-03-30].http://kns.cnki.net/kcms/detail/11.2378.TU.20200212.1135.002.html.
[9] 龙瀛.泛智慧城市技术提高城市韧性——应对2020新型冠状病毒肺炎突发事件笔谈会[J/OL].城市规划:1.[2020-03-30].http://kns.cnki.net/kcms/detail/11.2378.tu.20200211.2048.014.html.
[10] 潘教峰. 智库DIIS理论方法[M].北京:科学出版社,2019.
[11] Chen, PCL, Zhang, CY.Data-intensive applications, challenges, techniques and technologies: a survey on big data[J].Information Science,2014,275(11),314-347.
[12] Keohane, R., Nye, JS.Power and Interdependence[M]. Prentice Hall Inc., New Jersey, USA,2011.
[13] 巴志超,李纲,安璐,等.国家安全大数据综合信息集成:应用架构与实现路径[J].中国软科学,2018,331(7),14-25.
[14] 李品,杨国立,杨建林.面向国家安全与发展决策支持的情报服务体系框架研究[J].情报理论与实践,2020,43(02):9-14.
[15] 陈美华,陈峰.维护科技安全的情报感知路径探析[J].情报科学,2019,37(5),137-141.
[16] 董尹,赵小康.公开源情报研究综述[J].情报理论与实践,2013,1,122-127.
[17] 杨建英,余至诚.开源情报在中国国家安全情报中的地位和作用分析[J] 情报杂志,2019,38(10),21-26+145.
[18] 李辉. 新时代我国科技情报工作的价值定位与发展方略[J].科技情报研究,2019,1(1),51-63.
[19] 李林,廖晋平,张烜工.科技安全预警机制的建立及完善[J].科技导报,2019,37(19),26-32.
[20] 胡雅萍,刘千里,潘彬彬.维护科技安全的情报预测研究[J].情报杂志,2014,33(9),8-12.
[21] 中华人民共和国传染病防治法[EB/OL].[2020-02-12].http://www.gov.cn/banshi/2005-08/01/content_19023.htm.
[22] 陈安,周丹.突发事件机理体系与现代应急管理体制设计.安全,2019,40(7),16-23.
[23] 中华人民共和国突发事件应对法[EB/OL].http://www.gov.cn/ziliao/flfg/2007-08/30/content_732593.htm,2020-02-12.
[24] 中华人民共和国科学技术部,2020.科技部办公厅关于加强新型冠状病毒肺炎科技攻关项目管理有关事项的通知[EB/OL].[2020-02-12].http://www.sohu.com/a/369721616_120059213.
[25] 新冠肺炎疫情对若干行业的影响分析[R].上海交通大学行业研究院,新冠肺炎疫情对若干行业的影响分析[R].上海交通大学行业研究院,上海,2020.
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