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

Assimakis A. Kattis, Alexander Holiday, Ana Andreea Stoica, Ioannis G. Kevrekidis

Research output: Contribution to journalArticle

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)153-162
Number of pages10
JournalVirulence
Volume7
Issue number2
DOIs
StatePublished - Feb 17 2016

All Science Journal Classification (ASJC) codes

  • Parasitology
  • Microbiology
  • Immunology
  • Microbiology (medical)
  • Infectious Diseases

Keywords

  • SIS
  • adaptive networks
  • data mining
  • diffusion maps
  • epidemics
  • equation-free

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    Kattis, A. A., Holiday, A., Stoica, A. A., & Kevrekidis, I. G. (2016). Modeling epidemics on adaptively evolving networks: A data-mining perspective. Virulence, 7(2), 153-162. https://doi.org/10.1080/21505594.2015.1121357