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Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis

Author

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  • Xiaojin Xie

    (School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Kangyang Luo

    (School of Data Science and Engineering, East China Normal University, Shanghai 200062, China)

  • Zhixiang Yin

    (School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China)

  • Guoqiang Wang

    (School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China)

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has caused a global disaster, seriously endangering human health and the stability of social order. The purpose of this study is to construct a nonlinear combinational dynamic transmission rate model with automatic selection based on forecasting effective measure (FEM) and support vector regression (SVR) to overcome the shortcomings of the difficulty in accurately estimating the basic infection number R 0 and the low accuracy of single model predictions. We apply the model to analyze and predict the COVID-19 outbreak in different countries. First, the discrete values of the dynamic transmission rate are calculated. Second, the prediction abilities of all single models are comprehensively considered, and the best sliding window period is derived. Then, based on FEM, the optimal sub-model is selected, and the prediction results are nonlinearly combined. Finally, a nonlinear combinational dynamic transmission rate model is developed to analyze and predict the COVID-19 epidemic in the United States, Canada, Germany, Italy, France, Spain, South Korea, and Iran in the global pandemic. The experimental results show an the out-of-sample forecasting average error rate lower than 10.07% was achieved by our model, the prediction of COVID-19 epidemic inflection points in most countries shows good agreement with the real data. In addition, our model has good anti-noise ability and stability when dealing with data fluctuations.

Suggested Citation

  • Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2307-:d:638587
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    References listed on IDEAS

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