Koopman Representations for Early Outbreak Warning and Minimal Counterfactual Intervention in Multi-Agent Epidemic Simulations

2026-05-05 · Source: cs.MA updates on arXiv.org · Field: Science & Research — Artificial Intelligence & Machine Learning, Health & Medical Research, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

A Koopman-based framework has been developed for early outbreak detection and minimal counterfactual intervention within a multi-agent epidemic simulation. This simulation models agents with mobility patterns, heterogeneous susceptibility, immunity-dependent viral load progression, and local transmission. The framework focuses on near-critical epidemic regimes where small changes can alter outcomes. It encodes aggregate daily observables from early trajectory windows into a low-dimensional Koopman latent space, enabling short-horizon forecasting and outbreak risk estimation. These Koopman-derived features are then combined with a random forest classifier to predict if the final attack rate will exceed a 0.3 major outbreak threshold. Experiments demonstrate strong early warning performance, and counterfactual analysis shows that minimal interventions, such as quarantining a single agent for one day, can significantly reduce attack rates and prevent outbreaks in sensitive scenarios.

Key takeaway

For AI Scientists and Research Scientists developing epidemic models, this framework demonstrates how Koopman operator learning can provide early warning and identify effective, minimal interventions in complex multi-agent systems. You should consider integrating Koopman-derived features into your predictive models to enhance early detection of tipping points and explore counterfactual simulations to pinpoint critical intervention strategies, especially in scenarios where system dynamics are highly sensitive to small perturbations.

Key insights

Koopman representations enable early outbreak prediction and minimal intervention in complex multi-agent epidemic simulations.

Principles

• Nonlinear dynamics can be approximated by linear operators in a lifted Koopman space.
• Early aggregate signals contain sufficient information for long-term outcome prediction.
• Small, targeted perturbations can redirect near-critical system trajectories.

Method

The method involves generating multi-agent epidemic trajectories, learning Koopman latent representations from early aggregate observables, using these representations with a random forest for outbreak classification, and performing counterfactual interventions to test outcome modifiability.

In practice

• Use Koopman models to forecast aggregate epidemic observables from early data.
• Combine Koopman features with classifiers for robust outbreak risk assessment.
• Simulate single-agent, single-day quarantines to identify critical intervention points.

Topics

• Koopman Operator Learning
• Multi-Agent Epidemic Simulation
• Early Outbreak Detection
• Counterfactual Intervention
• Tipping Point Dynamics

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.