Abstract:
: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. Obtaining reduced, small size accurate models in terms of these few statistical observables--that is, trying to coarse-grain the full network epidemic model to a small but useful macroscopic one--is even more daunting. Here we describe a data-based approach to solving the first challenge: the detection of a few informative collective observables of the detailed epidemic dynamics. This is accomplished through Diffusion Maps (DMAPS), a recently developed data-mining technique. We illustrate the approach through simulations of a simple mathematical model of epidemics on a network: a model known to exhibit complex temporal dynamics. We discuss potential extensions of the approach, as well as possible shortcomings.
journal_name
Virulencejournal_title
Virulenceauthors
Kattis AA,Holiday A,Stoica AA,Kevrekidis IGdoi
10.1080/21505594.2015.1121357subject
Has Abstractpub_date
2016-01-01 00:00:00pages
153-62issue
2eissn
2150-5594issn
2150-5608journal_volume
7pub_type
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