# Toward World Models for Epidemiology

arXiv: https://arxiv.org/abs/2604.09519 · HTML: https://mlsanity.com/paper/2604.09519/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 19 of its 67 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 19 of 19)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://mlsanity.com/paper/2507.19457/) (2025)
- [A Comprehensive Survey on World Models for Embodied AI](https://mlsanity.com/paper/2510.16732/) (2025)
- [Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning](https://mlsanity.com/paper/2107.00848/) (2021)
- [Differentiable Agent-based Epidemiology](https://mlsanity.com/paper/2207.09714/) (2022)
- [A Framework for Evaluating Epidemic Forecasts](https://mlsanity.com/paper/1703.09828/) (2017)
- [Flow Equivariant World Models: Memory for Partially Observed Dynamic Environments](https://mlsanity.com/paper/2601.01075/) (2026)
- [Latent Action World Models for Control with Unlabeled Trajectories](https://mlsanity.com/paper/2512.10016/) (2025)
- [Deep Identification of Propagation Trees](https://mlsanity.com/paper/2503.00646/) (2025)
- [Planning as Inference in Epidemiological Models](https://mlsanity.com/paper/2003.13221/) (2020)
- [Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models](https://mlsanity.com/paper/2010.08478/) (2020)
- [How (Not) to Hybridize Neural and Mechanistic Models for Epidemiological Forecasting](https://mlsanity.com/paper/2602.06323/) (2026)
- [On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems](https://mlsanity.com/paper/2407.13091/) (2024)
- [Adversarial Data Modeling in Epidemiology](https://mlsanity.com/paper/2602.20134/) (2026)

## Built on by (most cited first, 3 of 3)

- [Design-Ignoring versus Design-Respecting World Models for Epidemiology](https://mlsanity.com/paper/2609.30679/) (2026)
- [Graph World Models for Constrained Epidemic Policy Planning](https://mlsanity.com/paper/2609.35545/) (2026)
- [Network World Models as Environments for Algorithm Design on Complex Systems](https://mlsanity.com/paper/2610.01048/) (2026)

## Abstract (arXiv)

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.