SL-BiLEM: Structured Learnable Behavior-in-the-Loop Epidemic Modeling for Forecasting and Policy Evaluation
arXiv:2605.26704v1 Announce Type: new Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distribution shifts at policy intervention points. This renders data-driven models unreliable under distribution shift. We propose \textbf{SL-BiLEM} (Structured Learnable Behavior-in-the-Loop Epidemic Model), leveraging physical constraints as regularization for robust extrapolation. The framework decomposes effective transmission as $\beta_{\text{eff}}(t,g) = \beta_0(g) \times m_{\text{policy}}(t) \times
This research addresses the ongoing challenge of accurate epidemic forecasting in the face of dynamic human behavior, a persistent issue highlighted by recent global health crises.
Accurate epidemic modeling that accounts for human behavior and policy intervention is crucial for public health, economic stability, and effective governance.
Epidemic models could become more robust and reliable, providing better foresight for policy decisions and reducing the economic and social disruption of future outbreaks.
- · Public Health Agencies
- · Governments
- · AI Researchers
- · Healthcare Sector
- · Companies reliant on inaccurate public health forecasts
- · Static modeling approaches
Improved epidemic forecasting models that account for human behavioral feedback loops.
More effective and timely public health interventions due to better predictive capabilities, potentially reducing disease spread and economic disruption.
Enhanced trust in government responses to health crises and potential for early warning systems that dynamically adapt to population behavior.
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