Network Meta-Metrics: Using Evolutionary Computation to Identify Effective Indicators of Epidemiological Vulnerability in a Livestock Production System Model

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@Article{Wiltshire:2019:JASSS,

• author = "Serge Wiltshire and Asim Zia and Christopher Koliba and Gabriela Bucini and Eric Clark and Scott Merrill and Julie Smith and Susan Moegenburg",
• title = "Network Meta-Metrics: Using Evolutionary Computation to Identify Effective Indicators of Epidemiological Vulnerability in a Livestock Production System Model",
• journal = "Journal of Artificial Societies and Social Simulation",
• year = "2019",
• volume = "22",
• number = "2",
• keywords = "genetic algorithms, genetic programming, agent-based modeling, network analytics, computational epidemiology, evolutionary computation, livestock production",
• ISSN = "1460-7425",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:jas:jasssj:2018-45-2",
• oai = "oai:RePEc:jas:jasssj:2018-45-2",
• URL = "http://jasss.soc.surrey.ac.uk/22/2/8/8.pdf",
• DOI = "doi:10.18564/jasss.3991",
• size = "27 pages",
• 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 {\^a}{$\euro$}{"} which we call {"}meta-metrics{"} {\^a}{$\euro$}{"} that may better predict vulnerability. We find that the GP solutions {\^a}{$\euro$}{"} the best of which combine both global and node -level metrics {\^a}{$\euro$}{"} are far better indicators of disease risk than any individual metric, with meta-metrics explaining up to 91 percent 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.",

}

Genetic Programming entries for Serge Wiltshire Asim Zia Christopher Koliba Gabriela Bucini Eric Clark Scott Merrill Julie Smith Susan Moegenburg

Citations