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

TL;DR

This paper introduces a Koopman-based method for early epidemic outbreak detection and minimal intervention strategies in multi-agent simulations, enabling effective risk prediction and outbreak mitigation.

Contribution

It develops a novel Koopman-based framework that encodes epidemic dynamics into a low-dimensional space for forecasting and intervention analysis.

Findings

Koopman features improve early outbreak risk prediction.

Minimal interventions can significantly reduce attack rates.

Counterfactual analysis identifies effective targeted interventions.

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.