Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15–17 March 2024, Changsha, China

Research Article

Technical Solutions for Risk Assessment and Emergency Response of Sudden Major Zoonotic Disease Outbreaks: Based on Machine Learning

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  • @INPROCEEDINGS{10.4108/eai.15-3-2024.2346517,
        author={Bo  Qin and Han  Diao and Yuncheng  Jia and Haoming  Xu},
        title={Technical Solutions for Risk Assessment and Emergency Response of Sudden Major Zoonotic Disease Outbreaks: Based on Machine Learning},
        proceedings={Proceedings of the 4th International Conference on Public Management and Intelligent Society, PMIS 2024, 15--17 March 2024, Changsha, China},
        publisher={EAI},
        proceedings_a={PMIS},
        year={2024},
        month={6},
        keywords={zoonotic diseases; risk assessment; emergency response; machine learning; artificial intelligence},
        doi={10.4108/eai.15-3-2024.2346517}
    }
    
  • Bo Qin
    Han Diao
    Yuncheng Jia
    Haoming Xu
    Year: 2024
    Technical Solutions for Risk Assessment and Emergency Response of Sudden Major Zoonotic Disease Outbreaks: Based on Machine Learning
    PMIS
    EAI
    DOI: 10.4108/eai.15-3-2024.2346517
Bo Qin1, Han Diao2, Yuncheng Jia3, Haoming Xu4,*
  • 1: University of Electronic Science and Technology of China
  • 2: Sichuan Academy of Social Sciences
  • 3: Chulalongkorn University
  • 4: Party School of Chengdu Municipal Committee of CPC
*Contact email: qinbo44@gmail.com

Abstract

Recent data from the World Health Organization (WHO) underscores that approximately 75% of reported global diseases are zoonotic, posing dual threats to both livestock industries and human health. Establishing an epidemic risk prediction model is imperative, leveraging cutting-edge technologies such as blockchain, artificial intelligence, and big data. This paper aims to devise emergency response strategies for major public health crises, vital for fortifying government prevention capabilities. It outlines plans to develop an epidemic risk assessment model, align emergency responses with TOPSIS evaluations, and implement an OODA (Observe, Orient, Decide, Act) emergency response framework. Additionally, the study investigates enhancing epidemic emergency measures in China through the integration of contemporary internet knowledge. By bridging theory with practice and leveraging advanced technologies, this research contributes to the development of comprehensive strategies for combating zoonotic diseases and safeguarding public health on a global scale.