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[2602.00299] Agentic Framework for Epidemiological Modeling
Summary
The paper introduces EPIAGENT, an innovative agentic framework for epidemiological modeling that automates the synthesis, calibration, and verification of disease models, enhancing public health planning.
Why It Matters
This framework addresses the limitations of traditional epidemiological models, which often require manual adjustments as conditions change. By automating model generation and ensuring correctness through an explicit flow graph representation, EPIAGENT can lead to more accurate and timely public health responses, particularly in the context of evolving pathogens and vaccination strategies.
Key Takeaways
- EPIAGENT automates the modeling process, improving efficiency.
- The framework uses an explicit epidemiological flow graph for model verification.
- It enhances the accuracy of epidemiological projections under varying assumptions.
- The agentic feedback loop mimics expert workflows, accelerating model convergence.
- EPIAGENT addresses the dynamic nature of public health challenges.
Computer Science > Machine Learning arXiv:2602.00299 (cs) [Submitted on 30 Jan 2026 (v1), last revised 26 Feb 2026 (this version, v2)] Title:Agentic Framework for Epidemiological Modeling Authors:Rituparna Datta, Zihan Guan, Baltazar Espinoza, Yiqi Su, Priya Pitre, Srini Venkatramanan, Naren Ramakrishnan, Anil Vullikanti View a PDF of the paper titled Agentic Framework for Epidemiological Modeling, by Rituparna Datta and 7 other authors View PDF HTML (experimental) 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 assu...
