DengueGNN: Graph-based deep learning for modeling disease spread dynamics and prediction

Dengue fever is a mosquito-borne disease that is rapidly spreading across the world due to climate, human mobility and other regional connections, making it a serious challenge for public health. Statistical and machine learning models that are commonly used do not adequately represent the complex spatio-temporal patterns of disease transmission and thus produce poor forecast and delayed response for dengue transmission. This study proposes Dynamic Spatio-Temporal Graph Neural Network (ST-GNN), which accurately predicts dengue fever regionally. The model produces a dynamic graph whose nodes are regions, the human mobility or adjacency is represented by edges, and other environmental and historical epidemiological characteristics are used for the graph nodes. Graph convolution layers are used for representing spatial dependence and attention augmented LSTMs are used for representing temporally evolving data. The graph convolution and attention layers make it possible to fuse the environmental and mobility features for the purpose of enhancing predictions. Experimental validation done on OpenDengue dataset indicates that ST-GNN outperforms the 10 baseline models with RMSE of 6.1, MAE of 4.9 and MAPE of 12.0% for a one-week prediction and RMSE of 10.9, MAE of 8.8 and MAPE of 22.4% for a four-week prediction with minimal RMSE observed for one-week prediction and highest correlation in space (Moran’s I: 0.76 and 0.68 respectively). To validate the importance of dynamic graphs, temporal attention and mobility features, we performed ablation experiments. In addition, the GNNExplainer and Integrated Gradients showed a region with high risk and key environmental drivers. Taken together, the proposed framework enables the graspable multi-horizon forecasting to guide proactive dengue prevention and corresponding public health interventions.