Inter- and Intra-Series Embeddings Fusion Network for Epidemiological Forecasting

TL;DR

This paper introduces SEFNet, a novel neural network architecture that fuses inter-region and intra-region embeddings to enhance epidemic forecasting accuracy, effectively capturing dynamic and temporal dependencies in multi-region epidemic data.

Contribution

The paper proposes SEFNet, a new fusion network combining multi-scale convolution, self-attention, LSTM, and autoregressive components for improved epidemic prediction.

Findings

SEFNet outperforms existing models on four epidemic datasets.

The fusion of inter- and intra-series embeddings improves prediction accuracy.

Incorporating autoregressive components enhances model robustness.

Abstract

The accurate forecasting of infectious epidemic diseases is the key to effective control of the epidemic situation in a region. Most existing methods ignore potential dynamic dependencies between regions or the importance of temporal dependencies and inter-dependencies between regions for prediction. In this paper, we propose an Inter- and Intra-Series Embeddings Fusion Network (SEFNet) to improve epidemic prediction performance. SEFNet consists of two parallel modules, named Inter-Series Embedding Module and Intra-Series Embedding Module. In Inter-Series Embedding Module, a multi-scale unified convolution component called Region-Aware Convolution is proposed, which cooperates with self-attention to capture dynamic dependencies between time series obtained from multiple regions. The Intra-Series Embedding Module uses Long Short-Term Memory to capture temporal relationships within each time series. Subsequently, we learn the influence degree of two embeddings and fuse them with the parametric-matrix fusion method. To further improve the robustness, SEFNet also integrates a traditional autoregressive component in parallel with nonlinear neural networks. Experiments on four real-world epidemic-related datasets show SEFNet is effective and outperforms state-of-the-art baselines.