Dynamical analysis of the infection status in diverse communities due to COVID-19 using a modified SIR model

STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information

The modern urban population features a high population density and a fast population flow, and COVID-19 has strong transmission ability, long incubation period, and other characteristics. Considering only the time sequence of COVID-19 transmission cannot effectively respond to the current epidemic transmission situation. The distance between cities and population density information also have a significant impact on the transmission of the virus. Currently, cross-domain transmission prediction models do not fully exploit the time-space information and fluctuation trend of data, and cannot reasonably predict the trend of infectious diseases by integrating time-space multi-source information. To solve this problem, this paper proposes the COVID-19 prediction network (STG-Net) based on multivariate spatio-temporal information, which introduces the Spatial Information Mining module (SIM) and the Temporal Information Mining module (TIM) to mine the spatio-temporal information of the data in a deeper level, and uses the slope feature method to further mine the fluctuation trend of the data. Also, we introduce the Gramian Angular Field module (GAF), which converts one-dimensional data into two-dimensional images, further enhancing the network's feature mining capability in the time and feature dimension, ultimately combining spatiotemporal information to predict daily newly confirmed cases. We tested the network on datasets from China, Australia, the United Kingdom, France, and Netherlands. The experimental results show that STG-Net has better prediction performance than existing prediction models, with an average decision coefficient R2 of 98.23% on the datasets from five countries, as well as good long- and short-term prediction ability and overall good robustness.