arXiv:2602.22270v2 Announce Type: replace Abstract: Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter estimation. To address these challenges, we propose the Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors. STOEP consists of three key components: (1) Case-aware Adjacency Learning (CAL), which dynamically adjusts mobility-b
The continuous evolution of AI in epidemiological modeling coincides with the ongoing need for more robust public health tools post-pandemic, leveraging advancements in machine learning to address past challenges.
Improved epidemic forecasting directly impacts public health management, resource allocation, and economic stability by providing more accurate and timely insights into disease spread, reducing the impact of future outbreaks.
The integration of implicit spatio-temporal priors and explicit expert priors into epidemic forecasting models like STOEP offers a more nuanced and accurate approach compared to previous oversimplified methods, leading to better predictive power.
- · Public health organizations
- · Epidemiologists
- · Healthcare systems
- · AI/ML researchers
- · Outdated forecasting methodologies
- · Regions without advanced data infrastructure
More precise and earlier interventions for epidemic control will become possible.
Economic disruption from future pandemics could be significantly mitigated through proactive measures based on superior forecasting.
The success of such models could lead to broader AI adoption in other complex societal challenges requiring spatio-temporal predictions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG