Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning

• URL: http://arxiv.org/abs/2606.22171v1
• Date: Sat, 20 Jun 2026 17:58:17 GMT
• Title: Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning
• Abstract summary: We introduce a spatially structured multimodal epidemic forecasting setting that integrates region-level temporal surveillance data with spatially localized auxiliary signals.<n>We evaluate our approach on real-world COVID-19, influenza, and influenza-like illness (IIL) forecasting tasks under realistic real-time evaluation protocols.
• Score: 50.948271066372826
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring sub-regional spatial structure that shapes disease dynamics. We introduce a spatially structured multimodal epidemic forecasting setting that integrates region-level temporal surveillance data with spatially localized auxiliary signals that are misaligned in resolution and structure, reflecting realistic public health reporting constraints. Building on this formulation, we propose M-SPICE (Multimodal SPatIal Context for Epidemic Forecasting), a structure-aware spatiotemporal forecasting framework that performs joint reasoning over temporal disease dynamics and spatial context via attention-based multimodal fusion, allowing spatial signals to selectively condition temporal representations across forecast horizons. We evaluate our approach on real-world COVID-19, influenza, and influenza-like illness (ILI) forecasting tasks under realistic real-time evaluation protocols. Across all forecasting settings, our method consistently outperforms state-of-the-art multivariate time-series, multimodal, and epidemiological forecasting baselines while maintaining strong probabilistic forecasting performance. Finally, interpretability analyses reveal when, where, and how spatial signals are leveraged, highlighting settings in which purely temporal, region-aggregated models are most likely to fail.

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