Transmission Process Prediction of Novel Coronavirus Based on System Dynamics

doi:10.16182/j.issn1004731x.joss.20-1019

Abstract

Abstract: The transmission characteristics of novel coronavirus is considered and a new SE4IR2 model based on the principle of system dynamics is proposed. The US epidemic data from June to November is used to set the isolation rate and other parameters, and the SE4IR2 model is used to fit, analyze and predict the development of the epidemic trend in the next stage. The empirical part uses the data from June to November in the United States to achieve the parameters of the SE4IR2 model, obtains the parameter values in December and January through the time series prediction model, and compares the predicted number of deaths and the people cured with those of the WTO published in December and January. The error in December is 0.75% and - 0.86%, and the error in January is 2.78% and 3.57%. Based on the results of the empirical analysis, considering the constraints between the various parameters, the corresponding recommendations for epidemic prevention and control are given. The results show that the SE4IR2 model has better simulation accuracy and is more suitable for simulating the spread of COVID-19.

Key words: COVID-19, system dynamics, dynamic programming, time series prediction, Auto Regression Moving Average

CLC Number:

TP391.9

Lu Xuepeng, Shang Jiao, Zhao Junhui, Lü Lulu, Zhou Li. Transmission Process Prediction of Novel Coronavirus Based on System Dynamics[J]. Journal of System Simulation, 2021, 33(7): 1713-1721.

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