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Application of Artificial Intelligence to Social Governance Capabilities under Public Health Emergencies

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  • Yafang Wu
  • Shaonan Shan

Abstract

Due to the high complexity, high destructive power, and comprehensive governance characteristics of public health emergencies, the ability of social governance has been distorted and alienated under intensive pressure, and the subjects of social governance have become lazy, professional, and politicized. There are obvious problems, such as system information leakage and information asymmetry. Based on the above background, the purpose of this article is to study the application of artificial intelligence to social governance capabilities under public health emergencies. This article focuses on the relevant concepts and content of emergency management of public health emergencies and in-depth analysis of the actual application of big data technology in epidemic traceability and prediction, medical diagnosis and vaccine research and development, people’s livelihood services, and government advice and suggestions, combined with investigations. The questionnaire analysis sorted out the problems in the social emergency management of public health emergencies in China. The results showed that 87.7% of the people simply sorted out laws and regulations and higher-level documents or even repeated content and lacked summary and reflection on emergency response experience, which led to the operability of emergency plans being generally even poor. In response to the shortcomings, countermeasures and suggestions were put forward, including establishing a standard data collection mechanism, establishing a data sharing mechanism, establishing a personal privacy security protection mechanism, and promoting the breadth and depth of big data applications.

Suggested Citation

  • Yafang Wu & Shaonan Shan, 2021. "Application of Artificial Intelligence to Social Governance Capabilities under Public Health Emergencies," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, February.
  • Handle: RePEc:hin:jnlmpe:6630483
    DOI: 10.1155/2021/6630483
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