SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting

• URL: http://arxiv.org/abs/2605.06530v1
• Date: Thu, 07 May 2026 16:31:43 GMT
• Title: SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
• Abstract summary: Accurate forecasting is crucial for public health response, resource allocation, and intervention.<n>Because epidemics unfold across regions, realistic epidemiological methods are natural candidates for improving forecasts.<n>Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple train chronological-test that do not reflect real-time forecasting practice.
• Score: 14.31847187460321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate epidemic forecasting is crucial for public health response, resource allocation, and outbreak intervention, but remains difficult with sparse, noisy, and highly non-stationary data. Because epidemics unfold across interacting regions, spatiotemporal methods are natural candidates for improving forecasts. Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple chronological train-test splits that do not reflect real-time forecasting practice. We address this gap with SpatialEpiBench, a challenging benchmark for spatiotemporal epidemic forecasting in realistic public-health settings. SpatialEpiBench includes 11 epidemic datasets with standardized rolling evaluations and outbreak-specific metrics. We evaluate adjacency-informed forecasting models with widely used epidemic priors that adapt general models to epidemiology, but find that most methods underperform a simple last-value baseline from 1 day to 1 month ahead, even during outbreaks and with these priors. We identify three major failure modes: (1) poor outbreak anticipation, (2) difficulty handling sparsity and noise, and (3) limited utility of common geographic adjacency for epidemiological spatial information. We release benchmark data, code, and instructions at https://github.com/Rachel-Lyu/SpatialEpiBench to support development of operationally useful epidemic forecasting models.

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