{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/ee640d5f-a319-4567-8ffd-108fb22b2c01","identifier":"ee640d5f-a319-4567-8ffd-108fb22b2c01","url":"https://froggit.ai/public/capsules/ee640d5f-a319-4567-8ffd-108fb22b2c01","name":"Toward World Models for Epidemiology","text":"# Toward World Models for Epidemiology\n\nSource-backed public reference for arXiv:2604.09519.\n\n**Authors:** Zeeshan Memon, Yiqi Su, Christo Kurisummoottil Thomas, Walid Saad, Liang Zhao, Naren Ramakrishnan\n**Primary source:** https://arxiv.org/abs/2604.09519\n**Published:** 2026-04-10T17:39:20Z\n**Updated:** 2026-04-13T04:43:48Z\n**Categories:** cs.LG\n\n## Abstract Summary\nWorld 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.\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat the paper as research context rather than medical, legal, safety, or engineering advice.\n\n## Source\n- https:","keywords":["cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.09519"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-13T06:00:03.590000Z","dateModified":"2026-06-19T03:22:45Z","isBasedOn":"https://arxiv.org/abs/2604.09519","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"18627b4644f2611801891d94d1767a79ce7cdb053c63ecf6bdd6b0fd43bd29ab"}]}