Multi-weight susceptible-infected model for predicting COVID-19 in China

Jun Zhang; Nanning Zheng; Mingyu Liu; Dingyi Yao; Yusong Wang; Jianji Wang; Jingmin Xin

doi:10.1016/j.neucom.2023.02.065

Multi-weight susceptible-infected model for predicting COVID-19 in China

Neurocomputing (Amst). 2023 May 14:534:161-170. doi: 10.1016/j.neucom.2023.02.065. Epub 2023 Mar 8.

Authors

Jun Zhang^{1

2}, Nanning Zheng¹, Mingyu Liu^{1

3}, Dingyi Yao^{1

3}, Yusong Wang¹, Jianji Wang¹, Jingmin Xin¹

Affiliations

¹ National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.
² School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.
³ Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.

Abstract

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.

Keywords: COVID-19 prediction; Data processing; Epidemic model; Multi-weight susceptible-infected model.