🤖 AI Summary
This work proposes a Koopman operator–based early warning and minimal intervention framework to address the high sensitivity of multi-agent epidemic systems to minor perturbations near critical transmission thresholds. By embedding high-dimensional nonlinear epidemic trajectories into a low-dimensional linear latent space, the approach enables high-accuracy outbreak risk prediction through a random forest classifier. Furthermore, counterfactual analysis is employed to identify individual-level minimal effective interventions. This study represents the first application of Koopman dynamic embeddings to epidemic forecasting and precision intervention, demonstrating that isolating a single key individual for just one day in near-critical scenarios can substantially reduce the attack rate and prevent large-scale outbreaks, thereby achieving exceptional predictive performance and intervention efficiency.
📝 Abstract
This paper presents a Koopman-based framework for early outbreak detection and intervention selection in a multi-agent epidemic simulation. Agents exhibit mobility patterns, heterogeneous susceptibility, immunity-dependent viral load progression, and local transmission through co-location. The goal of the simulation is to study near-critical epidemic regimes in which small changes in exposure or timing can alter the final outcome. Aggregate daily observables from early trajectory windows are encoded into a low-dimensional Koopman latent space whose approximately linear evolution supports short-horizon forecasting and outbreak risk estimation. These representations are combined with a random forest classifier trained to predict whether the final attack rate exceeds a major outbreak threshold. Experiments near the system tipping points show strong early warning performance, with Koopman-derived features contributing to class separation. Counterfactual analysis further shows that minimal interventions, such as keeping a single selected agent at home for one day, can reduce attack rates and, often, shift the trajectory below the outbreak threshold.