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

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 R0 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.

An Empirical Mode Decomposition Fuzzy Forecast Model for COVID-19

“…Xie et al replaced the basic infectious number with the combined dynamic transmission rate and proposed a nonlinear time-varying transmission rate model based on support vector regression. This method effectively solved the difficulties in accurately estimating the basic infectious number and the low prediction accuracy of single model in traditional epidemic epidemiology [ 36 ]. Gupta et al compared the classification methods of random forest, linear model, support vector machine, decision tree and neural network, and pointed out that random forest model was superior to other methods.…”

Global COVID-19 Epidemic Prediction and Analysis Based on Improved Dynamic Transmission Rate Model with Neural Networks

“…where k � t − t 0 represents the sliding window period, see [22,23,25,26] or Section 3.3 for details.…”

“…It is well-known that the fitting function plays an important role in the accuracy of prediction. Some well-known fitting functions have been considered in the literature, including the four-parameter polynomial function, the normal distribution function, the three-parameter exponential function, the three-parameter hyperbolic function, the two-parameter power function and the four-parameter logical function [11,22,23,25]. e details can be found in Table 1.…”

“…Subsequently, Hu et al [22,23] Compared with the SIR model [24], the SEIR model [2] and their extensions [5,6], the DTRM based on a data-driven approach has a wider application range, higher prediction accuracy and stronger robustness. Xie et al [25] Undoubtedly, the COVID-19 epidemic is a major public health emergency, that poses major challenge to the medical and health systems of all countries in the world and has a major impact on the global economic order. However, people are only concerned about the source, host, and spread of the COVID-19 epidemic, but the method of spreading, the pathogenic mechanism, the harmfulness, the lethality, the diagnosis and treatment plan, the treatment drugs, whether a given patient has sequelae after recovery, etc.…”

Predicting COVID-19 using lioness optimization algorithm and graph convolution network

“…Gumaei et al (2021) used gradient boosting regression (GBR) to build a training model to predict the total number of daily confirmed COVID-19 cases around the world. Xie et al (2021) proposed a nonlinear timevarying transmission rate model based on support vector regression in order to overcome the shortcomings of a single model for insufficient extraction of effective information and low prediction accuracy, and applied it to longterm prediction of COVID-19 epidemic in Hubei Province. Smith and Alvarez (2021) took COVID-19 patients in Wuhan, Hubei, China, as the research object and applied a series of machine learning models to analyze the factors affecting the death of COVID-19 patients.…”