Toward World Models for Epidemiology
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arXiv
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| Format: | Preprint |
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2026
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| author | Memon, Zeeshan Su, Yiqi Thomas, Christo Kurisummoottil Saad, Walid Zhao, Liang Ramakrishnan, Naren |
| author_facet | Memon, Zeeshan Su, Yiqi Thomas, Christo Kurisummoottil Saad, Walid Zhao, Liang Ramakrishnan, Naren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09519 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward World Models for Epidemiology Memon, Zeeshan Su, Yiqi Thomas, Christo Kurisummoottil Saad, Walid Zhao, Liang Ramakrishnan, Naren Machine Learning 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. |
| title | Toward World Models for Epidemiology |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.09519 |