Literature DB >> 21353716

Tools to study trends in community structure: application to fish and livestock trading networks.

Darren Michael Green1, Marleen Werkman, Lorna Ann Munro, Rowland Raymond Kao, István Zoltán Kiss, Leon Danon.   

Abstract

Partitioning of contact networks into communities allows groupings of epidemiologically related nodes to be derived, that could inform the design of disease surveillance and control strategies, e.g. contact tracing or design of 'firebreaks' for disease spread. However, these are only of merit if they persist longer than the timescale of interventions. Here, we apply different methods to identify concordance between network partitions across time for two animal trading networks, those of salmon in Scotland (2002-2004) and livestock in Great Britain (2003-2004). Both trading networks are similar in that they moderately agree over time in terms of their community structures, but this concordance is higher--and therefore community structure is more consistent--when only the 'core' network of nodes involved in trading over the whole time series is considered. In neither case was higher agreement found between partitions close together in time. These measures differ in their absolute values unless appropriate standardisation is applied. Once standardised, the measures gave similar values for both network types.
Copyright © 2011 Elsevier B.V. All rights reserved.

Mesh:

Year:  2011        PMID: 21353716     DOI: 10.1016/j.prevetmed.2011.01.008

Source DB:  PubMed          Journal:  Prev Vet Med        ISSN: 0167-5877            Impact factor:   2.670


  3 in total

1.  Network epidemiology and plant trade networks.

Authors:  Marco Pautasso; Mike J Jeger
Journal:  AoB Plants       Date:  2014-04-29       Impact factor: 3.276

2.  Modelling management strategies for a disease including undetected sub-clinical infection: bacterial kidney disease in Scottish salmon and trout farms.

Authors:  Alexander G Murray; Malcolm Hall; Lorna A Munro; I Stuart Wallace
Journal:  Epidemics       Date:  2011-10-14       Impact factor: 4.396

3.  Can biosecurity and local network properties predict pathogen species richness in the salmonid industry?

Authors:  Tadaishi Yatabe; Simon J More; Fiona Geoghegan; Catherine McManus; Ashley E Hill; Beatriz Martínez-López
Journal:  PLoS One       Date:  2018-01-30       Impact factor: 3.240

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.