Long-term prediction for temporal propagation of seasonal influenza using Transformer-based model

Influenza is one of the most common infectious diseases worldwide, which causes a considerable economic burden on hospitals and other healthcare costs. Predicting new and urgent trends in epidemiological data is an effective way to prevent influenza outbreaks and protect public health. Traditional autoregressive(AR) methods and new deep learning models like Recurrent Neural Network(RNN) have been actively studied to solve the problem. Most existing studies focus on the short-term prediction of influenza. Recently, Transformer models show superior performance in capturing long-range dependency than RNN models. In this paper, we develop a Transformer-based model, which utilizes the potential of the Transformer to increase the prediction capacity. To fuse information from data of different sources and capture the spatial dependency, we design a sources selection module based on measuring curve similarity. Our model is compared with the widely used AR models and RNNbased models on USA and Japan datasets. Results show that our approach provides approximate performance in short-term forecasting and better performance in long-term forecasting.

Automated summary

The study focuses on using a Transformer-based model to predict influenza outbreaks, showing superior performance in long-term forecasting compared to traditional AR and RNN models by using a source selection module based on curve similarity measurement to capture spatial dependency.