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    Rights statement: This is the author’s version of a work that was accepted for publication in Preventive Veterinary Medicine . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Preventive Veterinary Medicine, 150, 2018 DOI: 10.1016/j.prevetmed.2017.12.003

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Automatic classification of farms and traders in the pig production chain

Research output: Contribution to journalJournal articlepeer-review

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  • Lisa Koeppel
  • Tobias Siems
  • Mareike Fischer
  • Hartmut H.K. Lentz
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<mark>Journal publication date</mark>1/02/2018
<mark>Journal</mark>Preventive Veterinary Medicine
Volume150
Number of pages7
Pages (from-to)86-92
Publication StatusPublished
Early online date14/12/17
<mark>Original language</mark>English

Abstract

The trade in live pigs is an essential risk factor in the spread of animal diseases. Traders play a key role in the trade network, as they are logistics hubs and responsible for large animal movements. In order to implement targeted control measures in case of a disease outbreak, it is hence strongly advisable to use information about the holding type in the pig production chain. However, in many datasets the types of the producing farms or the fact whether the agent is a trader are unknown. In this paper we introduce two indices that can be used to identify the position of a producing farm in the pig production chain and more importantly, identify traders. This was realized partially through a novel dynamic programming algorithm. Analyzing the pig trade network in Germany from 2005 to 2007, we demonstrate that our algorithm is very sensitive in detecting traders. Since the methodology can easily be applied to trade networks in other countries with similar infrastructure and legislation, we anticipate its use for augmenting the datasets in further network analyses and targeting control measures. For further usage, we have developed an R package which can be found in the supplementary material to this manuscript.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Preventive Veterinary Medicine . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Preventive Veterinary Medicine, 150, 2018 DOI: 10.1016/j.prevetmed.2017.12.003