arXiv:2602.06323v2 Announce Type: replace Abstract: Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction. The mechanistic structure helps keep trajectories epidemiologically plausible, while neural components can capture non-stationary, data-adaptive effects. In practice, however, many seemingly straightforward couplings fail under partial observability and continually shifting transmission dynamics driven by behavior, waning immunity, seasonality, and interventions. We catalog these fail
The paper identifies ongoing challenges in hybrid AI model development for epidemiology, reflecting the current state of applied AI research and its limitations in complex, dynamic systems.
This highlights the practical difficulties and nuances in integrating different AI paradigms for real-world problems, suggesting that foundational challenges persist despite rapid advancements in AI.
The understanding of what constitutes a 'natural direction' for hybrid AI models shifts, emphasizing the need for more robust methods to handle partial observability and non-stationary dynamics.
- · AI researchers focusing on robust hybrid model design
- · Public health organizations leveraging improved forecasting
- · Overly simplistic hybrid AI model developers
- · Epidemiological forecasters relying on unproven methods
This research provides critical feedback for the development of more effective AI forecasting tools in epidemiology.
It may lead to a greater emphasis on explainability and robustness in AI models deployed for public health.
Improved epidemiological forecasting could enhance global preparedness for future health crises and reduce societal disruption.
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Read at arXiv cs.LG