Agentic Framework for Epidemiological Modeling

2026-01-30

AI Summary

This work proposes a novel approach to epidemic modeling by framing disease transmission as an iterative program synthesis problem, addressing the limitations of traditional models that rely on fixed structures and require extensive manual intervention to adapt to evolving pathogens, changing interventions, or shifting scenario assumptions. Central to this framework is an explicit epidemiological flow graph serving as an intermediate representation, which enables modular verification and interpretable parameter learning. By integrating agent-driven program synthesis, mechanism-based model compilation, and parameter optimization constrained by both physical and epidemiological principles, the method accurately captures complex transmission dynamics across diverse scenarios, generates counterfactual predictions grounded in epidemiological logic, and significantly accelerates convergence to high-quality models.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Reasoning under Uncertainty: Probabilistic Programming

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Epidemic modeling is essential for public health planning, yet traditional approaches rely on fixed model classes that require manual redesign as pathogens, policies, and scenario assumptions evolve. We introduce EPIAGENT, an agentic framework that automatically synthesizes, calibrates, verifies, and refines epidemiological simulators by modeling disease progression as an iterative program synthesis problem. A central design choice is an explicit epidemiological flow graph intermediate representation that links scenario specifications to model structure and enables strong, modular correctness checks before code is generated. Verified flow graphs are then compiled into mechanistic models supporting interpretable parameter learning under physical and epidemiological constraints. Evaluation on epidemiological scenario case studies demonstrates that EPIAGENT captures complex growth dynamics and produces epidemiologically consistent counterfactual projections across varying vaccination and immune escape assumptions. Our results show that the agentic feedback loop prevents degeneration and significantly accelerates convergence toward valid models by mimicking professional expert workflows.

Problem

• epidemiological modeling
• model adaptability
• scenario evolution
• manual redesign
• public health planning

Innovation

• agentic framework
• epidemiological modeling
• program synthesis
• flow graph
• mechanistic simulation