Network meta-metrics: Using evolutionary computation to identify effective indicators of epidemiological vulnerability in a livestock production system model

Authors

Serge Wiltshire, University of Vermont
Asim Zia, University of Vermont
Christopher Koliba, University of Vermont
Gabriela Buccini, University of Vermont
Eric Clark, University of Vermont
Scott Merrill, University of Vermont
Julie Smith, University of Vermont
Susan Moegenburg, University of Vermont

Document Type

Article

Publication Date

3-1-2019

Abstract

We developed an agent-based susceptible / infective model which simulates disease incursions in the hog production chain networks of three U.S. states. Agent parameters, contact network data, and epidemiological spread patterns are output after each model run. Key network metrics are then calculated, some of which pertain to overall network structure, and others to each node’s positionality within the network. We run statistical tests to evaluate the extent to which each network metric predicts epidemiological vulnerability, finding significant correlations in some cases, but no individual metric that serves as a reliable risk indicator. To investigate the complex interactions between network structure and node positionality, we use a genetic programming (GP) algorithm to search for mathematical equations describing combinations of individual metrics — which we call “meta-metrics” — that may better predict vulnerability. We find that the GP solutions — the best of which combine both global and node-level metrics — are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91% of the variability in agent vulnerability across all three study areas. We suggest that this methodology could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions, and also that the meta-metric approach may be useful to study a wide range of complex network phenomena.

Rights Information

© 2019, University of Surrey. All rights reserved.

Recommended Citation

Wiltshire S, Zia A, Koliba C, Bucini G, Clark E, Merrill S, Smith J, Moegenburg S. Network meta-metrics: Using evolutionary computation to identify effective indicators of epidemiological vulnerability in a livestock production system model. Journal of Artificial Societies and Social Simulation. 2019 Mar 31;22(2).

DOI

10.18564/jasss.3991