arXiv:2606.19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need. Reliable forecasts of epidemic time series can inform vaccination timing, hospital staffing, and resource allocation, yet the comparative behavior of modern forecasting architectures on infectious-disease surveillance data remains insufficiently characterized. We address this gap through a systematic evaluation of regional influenza forecasting using influenz
The increasing sophistication of AI models, particularly foundation models, is enabling more robust applications across various fields, including public health forecasting.
Improved time series forecasting for epidemics using advanced AI can significantly enhance public health responses, resource allocation, and preparedness for future health crises.
The ability to accurately forecast epidemic trends using foundation models could shift public health strategies from reactive to proactive, improving response efficacy and reducing societal impact.
- · Public health agencies
- · Healthcare providers
- · AI developers
- · Biomedical researchers
- · Traditional epidemiological modeling approaches
- · Regions with limited AI infrastructure
More accurate and timely public health interventions for epidemics.
Reduced morbidity and mortality from seasonal and emergent infectious diseases.
Increased investment in AI research and infrastructure for public health applications globally, potentially leading to new, AI-driven public health frameworks.
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