Toward World Models for Epidemiology

2026-04-10

AI Summary

This study addresses key challenges in epidemic decision-making, including hidden disease burdens, imperfect surveillance signals distorted by policy interventions, and intervention effects mediated by human behavior. It pioneers the systematic integration of the world model paradigm into computational epidemiology, framing epidemics as controlled partially observable dynamical systems. By jointly learning latent dynamics, endogenous observation mechanisms, and behavioral feedback loops, the approach enables counterfactual simulation and sequential decision planning under uncertainty. Evaluated across three case studies, the method effectively mitigates issues such as behavior-induced surveillance false positives and signal lags, demonstrating its necessity and superiority for policy evaluation and intervention analysis.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Game Theory and Economic Paradigms: Imperfect Information

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue that computational epidemiology is a natural and underdeveloped setting for world models. This is because epidemic decision-making requires reasoning about latent disease burden, imperfect and policy-dependent surveillance signals, and intervention effects are mediated by adaptive human behavior. We introduce a conceptual framework for epidemiological world models, formulating epidemics as controlled, partially observed dynamical systems in which (i) the true epidemic state is latent, (ii) observations are noisy and endogenous to policy, and (iii) interventions act as sequential actions whose effects propagate through behavioral and social feedback. We present three case studies that illustrate why explicit world modeling is necessary for policy-relevant reasoning: strategic misreporting in behavioral surveillance, systematic delays in time-lagged signals such as hospitalizations and deaths, and counterfactual intervention analysis where identical histories diverge under alternative action sequences.

Problem

• world models
• epidemiology
• latent dynamics
• policy-dependent surveillance
• intervention effects

Innovation

• world models
• computational epidemiology
• partially observed dynamical systems
• counterfactual reasoning
• behavioral feedback