Literature DB >> 21852007

The importance of location in contact networks: Describing early epidemic spread using spatial social network analysis.

Simon M Firestone1, Michael P Ward, Robert M Christley, Navneet K Dhand.   

Abstract

This paper explores methods for describing the dynamics of early epidemic spread and the clustering of infected cases in space and time when an underlying contact network structure is influencing disease spread. A novel method of describing an epidemic is presented that applies social network analysis to characterise the importance of both spatial location and contact network position. This method enables the development of a model of how these clusters formed, incorporating spatial clustering and contact network topology. Based on data from the first 30 days of the 2007 equine influenza outbreak in Australia, clusters of infected premises (IPs) were identified and delineated using social network analysis to integrate contact-tracing and spatial relationships. Clusters identified by this approach were compared to those detected using the space-time scan statistic to analyse the same data. To further investigate the importance of network and spatial location in epidemic spread, kriging by date of onset of clinical signs was used to model the spatio-temporal spread without reference to underlying contact network structure. Leave-one-out cross-validation was used to derive a summary estimate of the accuracy of the kriged surface. Observations poorly explained by the kriged surface were identified, and their position within the contact network was explored to determine if they could be better explained through analysis of the underlying contact network. The contact network was found to lie at the core of a combined network model of spread, with infected horse movements connecting spatial clusters of infected premises. Kriging was imprecise in describing the pattern of spread during this early phase of the outbreak (explaining only 13% of the variation in date of onset of IPs), because early dissemination was dominated by network-based spread. Combined analysis of spatial and contact network data demonstrated that over the first 30 days of this outbreak local spread emanated outwards from the small number of infected premises in the contact network, up to a distance of around 15km. Consequently, when a contact network structure underlies epidemic spread, we recommend applying a method of spatial analysis that incorporates the network component of disease spread. Linking the spatial and network analysis of epidemics facilitates inference of the methods of disease transmission, providing a description of the sequence of spatial cluster formation.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21852007     DOI: 10.1016/j.prevetmed.2011.07.006

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


  14 in total

1.  A longitudinal study describing horse demographics and movements during a competition season in Ontario, Canada.

Authors:  Kelsey L Spence; Terri L O'Sullivan; Zvonimir Poljak; Amy L Greer
Journal:  Can Vet J       Date:  2018-07       Impact factor: 1.008

2.  Network centrality for the identification of biomarkers in respondent-driven sampling datasets.

Authors:  Jacob Grubb; Derek Lopez; Bhuvaneshwar Mohan; John Matta
Journal:  PLoS One       Date:  2021-08-24       Impact factor: 3.240

3.  The influence of meteorology on the spread of influenza: survival analysis of an equine influenza (A/H3N8) outbreak.

Authors:  Simon M Firestone; Naomi Cogger; Michael P Ward; Jenny-Ann L M L Toribio; Barbara J Moloney; Navneet K Dhand
Journal:  PLoS One       Date:  2012-04-20       Impact factor: 3.240

4.  Evaluation of farm-level parameters derived from animal movements for use in risk-based surveillance programmes of cattle in Switzerland.

Authors:  Sara Schärrer; Stefan Widgren; Heinzpeter Schwermer; Ann Lindberg; Beatriz Vidondo; Jakob Zinsstag; Martin Reist
Journal:  BMC Vet Res       Date:  2015-07-14       Impact factor: 2.741

5.  Incorporation of spatial interactions in location networks to identify critical geo-referenced routes for assessing disease control measures on a large-scale campus.

Authors:  Tzai-Hung Wen; Wei Chien Benny Chin
Journal:  Int J Environ Res Public Health       Date:  2015-04-14       Impact factor: 3.390

6.  VetCompass Australia: A National Big Data Collection System for Veterinary Science.

Authors:  Paul McGreevy; Peter Thomson; Navneet K Dhand; David Raubenheimer; Sophie Masters; Caroline S Mansfield; Timothy Baldwin; Ricardo J Soares Magalhaes; Jacquie Rand; Peter Hill; Anne Peaston; James Gilkerson; Martin Combs; Shane Raidal; Peter Irwin; Peter Irons; Richard Squires; David Brodbelt; Jeremy Hammond
Journal:  Animals (Basel)       Date:  2017-09-26       Impact factor: 2.752

7.  Descriptive and network analyses of the equine contact network at an equestrian show in Ontario, Canada and implications for disease spread.

Authors:  Kelsey L Spence; Terri L O'Sullivan; Zvonimir Poljak; Amy L Greer
Journal:  BMC Vet Res       Date:  2017-06-21       Impact factor: 2.741

8.  Dynamic network measures reveal the impact of cattle markets and alpine summering on the risk of epidemic outbreaks in the Swiss cattle population.

Authors:  Beatriz Vidondo; Bernhard Voelkl
Journal:  BMC Vet Res       Date:  2018-03-13       Impact factor: 2.741

Review 9.  What can mathematical models bring to the control of equine influenza?

Authors:  J M Daly; J R Newton; J L N Wood; A W Park
Journal:  Equine Vet J       Date:  2013-08-02       Impact factor: 2.888

10.  Controlling infectious disease through the targeted manipulation of contact network structure.

Authors:  M Carolyn Gates; Mark E J Woolhouse
Journal:  Epidemics       Date:  2015-03-06       Impact factor: 4.396

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