{"paper":{"title":"Modeling epidemics on adaptively evolving networks: a data-mining perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.soc-ph","stat.AP"],"primary_cat":"q-bio.QM","authors_text":"Alexander Holiday, Ana-Andreea Stoica, Assimakis A. Kattis, Ioannis G. Kevrekidis","submitted_at":"2015-06-25T21:35:00Z","abstract_excerpt":"The exploration of epidemic dynamics on dynamically evolving (\"adaptive\") networks poses nontrivial challenges to the modeler, such as the determination of a small number of informative statistics of the detailed network state (that is, a few \"good observables\") that usefully summarize the overall (macroscopic, systems level) behavior. Trying to obtain reduced, small size, accurate models in terms of these few statistical observables - that is, coarse-graining the full network epidemic model to a small but useful macroscopic one - is even more daunting. Here we describe a data-based approach t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01452","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}