Journal of Information Resources Management ›› 2022, Vol. 12 ›› Issue (3): 118-136.doi: 10.13365/j.jirm.2022.03.118

Special Issue: 数字经济时代信息技术在应急管理中的理论与实践

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Big Data-Driven Public Health Risk Surveillance: Theoretical Framework and Practical Reflection

Wang Chao1 Xu Wendong2  Shi Ruyi1 Yu Xiaodong1   

  1. 1.School of Public Policy & Management,China University of Mining and Technology,Xuzhou,221116;
    2.School of Foreign Studies,China University of Mining and Technology,Xuzhou,221116
  • Online:2022-05-26 Published:2022-06-26

Abstract: Big data-driven public health risk surveillance has become one of the most active research fields of public health governance. However, the existing empirical evidence shows that the practical effect of big data surveillance is still insufficient. From the perspective of interactions between big data governance and risk surveillance,this paper aims to construct a theoretical framework of big data-driven public health risk surveillance. It reviews the progress of big data application in public health risk surveillance from the aspects of data sources, participants, model algorithms, surveillance systems and global cooperation networks. Then, it summarizes the present practical dilemma from the aspects of risk attributes, technical standards, regional economic differences, and participants’ awareness and ability. In the future, academia and practitioners need to focus on public health big data from exploring theoretical paradigms, building mutual trust relationships, optimizing surveillance mechanisms, exploring evidence-based decision-making, and integrating surveillance systems, so as to jointly promote the development of the new practice mode of public health risk surveillance driven by big data. At a time when the global epidemic prevention and control situation is still severe, the theoretical framework and practical reflection provided will help the academic community to form a clearer empirical understanding of big data-driven public health risk surveillance.

Key words: Big data-driven, Public health risk, Epidemic surveillance, Theoretical framework, Practical reflection

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