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A comparison of livestock movement network-methods and implications for disease control in developing countries

Dataset

Related Edinburgh Organisations

PublisherEdinburgh DataShare
Temporal coverageJan 2005 - 1 Jan 2014
Date made available18 Mar 2020
Geographical coverageCameroon,CM,CAMEROON

Abstract

The use of network analysis to support livestock disease control in low middle-income countries (LMICs) has historically been hampered by the cost of generating empirical data in the absence of animal movement recording schemes. To fill this gap, we have adopted methods which exploit freely available demographic and archived molecular data to generate livestock networks based on phylogeographic and gravity modelling techniques. We compare output from these network methodologies to empirical and randomly generated data. We simulate disease scenarios on the networks to evaluate the potential utility of our methodologies to inform robust livestock disease control strategies.
The molecular network was the closest approximation to the empirical network, both in relation to topological and epidemic characteristics. The gravity network tended to overestimated disease epidemics. However, better agreement across all three networks was observed if less specific epidemic characteristics such as the size of outbreak were investigated. Moreover, these methods consistently identified the same important animal movement and trade hotspots as the empirical networks. We therefore consider this proof-of-concept that demographic data such as censuses and archived molecular data could be repurposed to inform livestock disease management in LMICs.

Data Citation

Muwonge, Adrian; Bessell, Paul; Porphyre, Thibaud; Motta, Paolo; Rydevik, Gustaf; Devailly, Guillaume; Egbe, Franklyn; Kelly, Robert; Handel, Ian; Mazeri, Stella; Bronsvoort, Mark. (2020). A comparison of livestock movement network-methods and implications for disease control in developing countries, 2005-2014 [text]. University of Edinburgh. Roslin Institute. Genetics and Genomics. https://doi.org/10.7488/ds/2780.

ID: 142707424