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

Xiaojin Xie, Kangyang Luo, Zhixiang Yin and Guoqiang Wang
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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

Mathematics, 2021, vol. 9, issue 18, 1-17

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.

Keywords: COVID-19; SARS-CoV-2; dynamic transmission rate; forecasting effective measure; combined prediction model; support vector regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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