Forecasting Epidemic Spread With Recurrent Graph Gate Fusion Transformers

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as graph neural networks (GNNs), recurrent neural networks (RNNs), and Transformers, have enhanced predictions by modeling regional interactions, managing autoregressive time series, and identifying long-term dependencies. However, prior works often feature shallow integration of these models, leading to simplistic graph embeddings and inadequate analysis across different graph types. Additionally, excessive reliance on historical COVID-19 data limits the potential of utilizing time-lagged data, such as intervention policy information. To address these challenges, we introduce ReGraFT, a novel sequence-to-sequence (Seq2Seq) model designed for robust long-term forecasting of COVID-19. ReGraFT integrates multigraph-gated recurrent units (MGRU) with adaptive graphs, leveraging data from individual states, including infection rates, policy changes, and interstate travel. First, ReGraFT employs adaptive MGRU cells within an RNN framework to capture inter-regional dependencies, dynamically modeling complex transmission dynamics. Second, the model features a self-normalizing priming (SNP) layer using Scaled Exponential Linear Units (SeLU) to enhance stability and accuracy across short, medium, and long-term forecasts. Third, ReGraFT systematically compares and integrates various graph types, such as fully connected layers, pooling mechanisms, and attention-based structures, to provide a nuanced representation of inter-regional relationships. By incorporating lagged COVID-19 policy data, ReGraFT refines forecasts, demonstrating a 2.39% to 35.92% reduction in the root mean square error (RMSE) compared to state-of-the-art models. This work provides accurate long-term predictions, aiding in better public health decisions.