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[2602.21588] ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning
Summary
The paper presents ABM-UDE, a method for creating efficient surrogates for epidemic agent-based models using scientific machine learning, significantly improving computational speed and accuracy.
Why It Matters
This research addresses the challenge of slow epidemic modeling, which hampers timely decision-making in healthcare. By developing fast surrogates, it enhances the ability to conduct real-time scenario planning and supports critical decision-making in public health.
Key Takeaways
- ABM-UDE surrogates reduce computational time by approximately 10,000 times compared to traditional agent-based models.
- The method improves accuracy and reliability of epidemic forecasts, aiding in effective public health decision-making.
- PEM-UDE enhances model calibration, providing better uncertainty quantification in predictions.
Computer Science > Machine Learning arXiv:2602.21588 (cs) [Submitted on 25 Feb 2026] Title:ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning Authors:Sharv Murgai, Utkarsh Utkarsh, Kyle C. Nguyen, Alan Edelman, Erin C. S. Acquesta, Christopher Vincent Rackauckas View a PDF of the paper titled ABM-UDE: Developing Surrogates for Epidemic Agent-Based Models via Scientific Machine Learning, by Sharv Murgai and 5 other authors View PDF HTML (experimental) Abstract:Agent-based epidemic models (ABMs) encode behavioral and policy heterogeneity but are too slow for nightly hospital planning. We develop county-ready surrogates that learn directly from exascale ABM trajectories using Universal Differential Equations (UDEs): mechanistic SEIR-family ODEs with a neural-parameterized contact rate $\kappa_\phi(u,t)$ (no additive residual). Our contributions are threefold: we adapt multiple shooting and an observer-based prediction-error method (PEM) to stabilize identification of neural-augmented epidemiological dynamics across intervention-driven regime shifts; we enforce positivity and mass conservation and show the learned contact-rate parameterization yields a well-posed vector field; and we quantify accuracy, calibration, and compute against ABM ensembles and UDE baselines. On a representative ExaEpi scenario, PEM-UDE reduces mean MSE by 77% relative to single-shooting UDE (3.00 vs. 13.14) and by 20% relative to MS-UDE (3.75). Reliability i...
