@article{57736,
  author = {Assimakis A. Kattis and Alexander Holiday and Ana-Andreea Stoica and Ioannis Kevrekidis},
  title = {Modeling epidemics on adaptively evolving networks: A data-mining perspective},
  abstract = { <p>
	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.
</p>
 },
  year = {2016},
  journal = {Virulence},
  volume = {7},
  number = {2},
  pages = {153-162},
  month = {02/2016},
  isbn = {2150-5594},
  url = {https://doi.org/10.1080/21505594.2015.1121357},
  language = {eng},
}