Prediction of Disease Transmission Risk in Universities Based on SEIR and Multi-hidden Layer Back-propagation Neural Network Model

Abstract

Against the background of regular epidemic prevention and control, in order to ensure the return of teachers to work, students to return to school and safe operation of schools, the risk of disease transmission is analyzed in key areas such as university canoons, auditoriums, teaching buildings and dormitories. The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model, considering the four groups of people, namely susceptible, latent, infected and displaced, and their mutual transformation relationship. After feature post-processing, the selected feature parameters are processed with monotone non-decreasing and smoothing, and used as noise-free samples of stacked sparse denoising automatic coding network to train the network. Then, the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network, and these features are used as tags to carry out fitting training for the network. The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people, reduce the transmission rate of single contact, and reduce the peak number of infected people and latent people by 61% and 72% respectively, effectively controlling the disease spread in key regions of universities. Our method is able to accurately predict the number of infections.