Literature DB >> 26708252

Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control.

Kimberly L VanderWaal1, Catalina Picasso2, Eva A Enns3, Meggan E Craft4, Julio Alvarez5, Federico Fernandez6, Andres Gil7, Andres Perez8, Scott Wells9.   

Abstract

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Disease risk,; Livestock movement; Pathogen transmission; Social network analysis; Surveillance

Mesh:

Year:  2015        PMID: 26708252     DOI: 10.1016/j.prevetmed.2015.12.003

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


  19 in total

1.  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

2.  Evaluating empirical contact networks as potential transmission pathways for infectious diseases.

Authors:  Kimberly VanderWaal; Eva A Enns; Catalina Picasso; Craig Packer; Meggan E Craft
Journal:  J R Soc Interface       Date:  2016-08       Impact factor: 4.118

3.  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

4.  The Potential Role of Direct and Indirect Contacts on Infection Spread in Dairy Farm Networks.

Authors:  Gianluigi Rossi; Giulio A De Leo; Stefano Pongolini; Silvano Natalini; Luca Zarenghi; Matteo Ricchi; Luca Bolzoni
Journal:  PLoS Comput Biol       Date:  2017-01-26       Impact factor: 4.475

5.  Translating Big Data into Smart Data for Veterinary Epidemiology.

Authors:  Kimberly VanderWaal; Robert B Morrison; Claudia Neuhauser; Carles Vilalta; Andres M Perez
Journal:  Front Vet Sci       Date:  2017-07-17

6.  Seasonality and pathogen transmission in pastoral cattle contact networks.

Authors:  Kimberly VanderWaal; Marie Gilbertson; Sharon Okanga; Brian F Allan; Meggan E Craft
Journal:  R Soc Open Sci       Date:  2017-12-06       Impact factor: 2.963

7.  Modelling farm-to-farm disease transmission through personnel movements: from visits to contacts, and back.

Authors:  Gianluigi Rossi; Rebecca L Smith; Stefano Pongolini; Luca Bolzoni
Journal:  Sci Rep       Date:  2017-05-24       Impact factor: 4.379

8.  Optimal surveillance strategies for bovine tuberculosis in a low-prevalence country.

Authors:  Kimberly VanderWaal; Eva A Enns; Catalina Picasso; Julio Alvarez; Andres Perez; Federico Fernandez; Andres Gil; Meggan Craft; Scott Wells
Journal:  Sci Rep       Date:  2017-06-23       Impact factor: 4.379

9.  DNA multigene characterization of Fasciola hepatica and Lymnaea neotropica and its fascioliasis transmission capacity in Uruguay, with historical correlation, human report review and infection risk analysis.

Authors:  María Dolores Bargues; Valeria Gayo; Jaime Sanchis; Patricio Artigas; Messaoud Khoubbane; Soledad Birriel; Santiago Mas-Coma
Journal:  PLoS Negl Trop Dis       Date:  2017-02-03

10.  Using Machine Learning to Predict Swine Movements within a Regional Program to Improve Control of Infectious Diseases in the US.

Authors:  Pablo Valdes-Donoso; Kimberly VanderWaal; Lovell S Jarvis; Spencer R Wayne; Andres M Perez
Journal:  Front Vet Sci       Date:  2017-01-19
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