Modeling epidemics on adaptively evolving networks: A data-mining perspective

Citation:

Kattis, A. A. ; Holiday, A. ; Stoica, A. - A. ; Kevrekidis, I. G. Modeling epidemics on adaptively evolving networks: A data-mining perspective. Virulence 2016, 7 153-162.

Date Published:

Feb 17

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.

Notes:

Times Cited: 112150-5608

Last updated on 05/02/2016