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

Abstract

The cross-regional spread of COVID-19 had a huge impact on the normal global social order. This paper aims to build an improved dynamic transmission rate model based on the conjugate gradient neural network predicting and analyzing the global COVID-19 epidemic. First, we conduct an exploratory analysis of the COVID-19 epidemic from Canada, Germany, France, the United States, South Korea, Iran, Spain, and Italy. Second, a two-parameter power function is used for the nonlinear fitting of the dynamic transmission rate on account of data-driven approaches. Third, we correct the residual error and construct an improved nonlinear dynamic transmission rate model utilizing the conjugate gradient neural network. Finally, the inflection points of the global COVID-19 epidemic and new outbreaks, as well as the corresponding existing cases are predicted under the optimal sliding window period. The experimental results show that the model presented in this paper has higher prediction accuracy and robustness than some other existing methods.