Improving Dengue Forecasting with Spatiotemporal Data Augmentation and Machine Learning

Accurate forecasting of vector-borne diseases such as dengue is often challenged by limited and noisy spatiotemporal data. This study evaluates the effectiveness of data augmentation techniques in enhancing the robustness and predictive accuracy of machine learning models. We assess multiple augmentation strategies applied to weekly dengue case data across countries in South and Central America (2014–2022). Results show that augmentation substantially improves short-term forecasting performance, particularly in regions with sparse or irregular observations, yielding higher R² values and lower relative errors compared to non-augmented baselines. These findings demonstrate that well‑designed augmentation can mitigate data scarcity and strengthen the generalization of graph‑based deep learning frameworks for epidemiological forecasting. Overall, the study highlights augmentation as a practical and scalable approach for improving spatiotemporal ML applications in disease surveillance.