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A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area

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Listed:
  • Qi Yan

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Siqing Shan

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Menghan Sun

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Feng Zhao

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Yangzi Yang

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

  • Yinong Li

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operation, Beijing 100191, China)

Abstract

Accurately predicting the number of severe and critical COVID-19 patients is critical for the treatment and control of the epidemic. Social media data have gained great popularity and widespread application in various research domains. The viral-related infodemic outbreaks have occurred alongside the COVID-19 outbreak. This paper aims to discover trustworthy sources of social media data to improve the prediction performance of severe and critical COVID-19 patients. The innovation of this paper lies in three aspects. First, it builds an improved prediction model based on machine learning. This model helps predict the number of severe and critical COVID-19 patients on a specific urban or regional scale. The effectiveness of the prediction model, shown as accuracy and satisfactory robustness, is verified by a case study of the lockdown in Hubei Province. Second, it finds the transition path of the impact of social media data for predicting the number of severe and critical COVID-19 patients. Third, this paper provides a promising and powerful model for COVID-19 prevention and control. The prediction model can help medical organizations to realize a prediction of COVID-19 severe and critical patients in multi-stage with lead time in specific areas. This model can guide the Centers for Disease Control and Prevention and other clinic institutions to expand the monitoring channels and research methods concerning COVID-19 by using web-based social media data. The model can also facilitate optimal scheduling of medical resources as well as prevention and control policy formulation.

Suggested Citation

  • Qi Yan & Siqing Shan & Menghan Sun & Feng Zhao & Yangzi Yang & Yinong Li, 2022. "A Social Media Infodemic-Based Prediction Model for the Number of Severe and Critical COVID-19 Patients in the Lockdown Area," IJERPH, MDPI, vol. 19(13), pages 1-16, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:8109-:d:854003
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    References listed on IDEAS

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    3. Jayson S. Jia & Xin Lu & Yun Yuan & Ge Xu & Jianmin Jia & Nicholas A. Christakis, 2020. "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Nature, vol. 582(7812), pages 389-394, June.
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    1. Siqing Shan & Feng Zhao & Menghan Sun & Yinong Li & Yangzi Yang, 2022. "Suit the Remedy to the Case—The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior," IJERPH, MDPI, vol. 19(23), pages 1-18, December.

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