Literature DB >> 19267879

A review of network analysis terminology and its application to foot-and-mouth disease modelling and policy development.

C Dubé1, C Ribble, D Kelton, B McNab.   

Abstract

Livestock movements are important in spreading infectious diseases and many countries have developed regulations that require farmers to report livestock movements to authorities. This has led to the availability of large amounts of data for analysis and inclusion in computer simulation models developed to support policy formulation. Social network analysis has become increasingly popular to study and characterize the networks resulting from the movement of livestock from farm-to-farm and through other types of livestock operations. Network analysis is a powerful tool that allows one to study the relationships created among these operations, providing information on the role that they play in acquiring and spreading infectious diseases, information that is not readily available from more traditional livestock movement studies. Recent advances in the study of real-world complex networks are now being applied to veterinary epidemiology and infectious disease modelling and control. A review of the principles of network analysis and of the relevance of various complex network theories to infectious disease modelling and control is presented in this paper.

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Year:  2009        PMID: 19267879     DOI: 10.1111/j.1865-1682.2008.01064.x

Source DB:  PubMed          Journal:  Transbound Emerg Dis        ISSN: 1865-1674            Impact factor:   5.005


  31 in total

1.  Optimizing surveillance for livestock disease spreading through animal movements.

Authors:  Paolo Bajardi; Alain Barrat; Lara Savini; Vittoria Colizza
Journal:  J R Soc Interface       Date:  2012-06-22       Impact factor: 4.118

2.  Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies.

Authors:  G L Chaters; P C D Johnson; S Cleaveland; J Crispell; W A de Glanville; T Doherty; L Matthews; S Mohr; O M Nyasebwa; G Rossi; L C M Salvador; E Swai; R R Kao
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

3.  Social network analysis of patient sharing among hospitals in Orange County, California.

Authors:  Bruce Y Lee; Sarah M McGlone; Yeohan Song; Taliser R Avery; Stephen Eubank; Chung-Chou Chang; Rachel R Bailey; Diane K Wagener; Donald S Burke; Richard Platt; Susan S Huang
Journal:  Am J Public Health       Date:  2011-02-17       Impact factor: 9.308

4.  Descriptive network analysis of a Standardbred horse training facility contact network: Implications for disease transmission.

Authors:  Tanya M Rossi; Rachael M Milwid; Alison Moore; Terri L O'Sullivan; Amy L Greer
Journal:  Can Vet J       Date:  2020-08       Impact factor: 1.008

5.  Animal movement in a pastoralist population in the Maasai Mara Ecosystem in Kenya and implications for pathogen spread and control.

Authors:  George P Omondi; Vincent Obanda; Kimberly VanderWaal; John Deen; Dominic A Travis
Journal:  Prev Vet Med       Date:  2021-01-05       Impact factor: 2.670

6.  Dynamical patterns of cattle trade movements.

Authors:  Paolo Bajardi; Alain Barrat; Fabrizio Natale; Lara Savini; Vittoria Colizza
Journal:  PLoS One       Date:  2011-05-18       Impact factor: 3.240

7.  Impact of regulatory perturbations to disease spread through cattle movements in Great Britain.

Authors:  Matthew C Vernon; Matt J Keeling
Journal:  Prev Vet Med       Date:  2012-02-08       Impact factor: 2.670

8.  Modelling of paratuberculosis spread between dairy cattle farms at a regional scale.

Authors:  Gaël Beaunée; Elisabeta Vergu; Pauline Ezanno
Journal:  Vet Res       Date:  2015-09-25       Impact factor: 3.683

9.  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

10.  On the robustness of in- and out-components in a temporal network.

Authors:  Mario Konschake; Hartmut H K Lentz; Franz J Conraths; Philipp Hövel; Thomas Selhorst
Journal:  PLoS One       Date:  2013-02-06       Impact factor: 3.240

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