Static network analysis of a pork supply chain in Northern Germany—Characterisation of the potential spread of infectious diseases via animal movements

Büttner, Kathrin; Krieter, Joachim; Traulsen, Arne; Traulsen, Imke

doi:10.1016/j.prevetmed.2013.01.008

Cited by 75 publications

(99 citation statements)

References 16 publications

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“…Several contact network studies involving swine farms have demonstrated the hierarchical structure of the swine industry, where nursery and finishing farms must be considered to be highly vulnerable in terms of acquiring infection, while farrowing farms are the least vulnerable in terms of acquiring an infection but if infected become high risk farms for the on-going spread of any epidemic (Rautureau et al, 2012;Büttner et al, 2013;Dorjee et al, 2013). However, one contrasting finding from the present study was that when both direct and indirect contacts between farms were considered in the model, the hierarchical structure of the swine industry was no longer such an important determinant in the spread of disease.…”

Section: Discussionmentioning

confidence: 99%

Simulation of between-farm transmission of porcine reproductive and respiratory syndrome virus in Ontario, Canada using the North American Animal Disease Spread Model

Thakur

Revie

Hurnik

et al. 2015

Preventive Veterinary Medicine

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Section: Discussionmentioning

confidence: 99%

Simulation of between-farm transmission of porcine reproductive and respiratory syndrome virus in Ontario, Canada using the North American Animal Disease Spread Model

Thakur

Revie

Hurnik

et al. 2015

Preventive Veterinary Medicine

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“…To ensure early detection, management and control of any future emerging infectious disease outbreak, knowledge of the degree of interconnection as well as on spatial and temporal aspects of animal movements between non-commercial and commercial producers is therefore critical. However, such information is rare, as most information collected, is based on large commercial farms [5,7,10] or is obtained from countries where non-commercial pig farming is limited [11-14]. For instance, 5%, 11%, 15% and 19% of registered farms report less than 10 pigs in Denmark, Netherlands, Belgium and Sweden, respectively [15].…”

Section: Introductionmentioning

confidence: 99%

How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis

et al. 2014

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BackgroundThe impact of non-commercial producers on disease spread via livestock movement is related to their level of interaction with other commercial actors within the industry. Although understanding these relationships is crucial in order to identify likely routes of disease incursion and transmission prior to disease detection, there has been little research in this area due to the difficulties of capturing movements of small producers with sufficient resolution. Here, we used the Scottish Livestock Electronic Identification and Traceability (ScotEID) database to describe the movement patterns of different pig production systems which may affect the risk of disease spread within the swine industry. In particular, we focused on the role of small pig producers.ResultsBetween January 2012 and May 2013, 23,169 batches of pigs were recorded moving animals between 2382 known unique premises. Although the majority of movements (61%) were to a slaughterhouse, the non-commercial and the commercial sectors of the Scottish swine industry coexist, with on- and off-movement of animals occurring relatively frequently. For instance, 13% and 4% of non-slaughter movements from professional producers were sent to a non-assured commercial producer or to a small producer, respectively; whereas 43% and 22% of movements from non-assured commercial farms were sent to a professional or a small producer, respectively. We further identified differences between producer types in several animal movement characteristics which are known to increase the risk of disease spread. Particularly, the distance travelled and the use of haulage were found to be significantly different between producers.ConclusionsThese results showed that commercial producers are not isolated from the non-commercial sector of the Scottish swine industry and may frequently interact, either directly or indirectly. The observed patterns in the frequency of movements, the type of producers involved, the distance travelled and the use of haulage companies provide insights into the structure of the Scottish swine industry, but also highlight different features that may increase the risk of infectious diseases spread in both Scotland and the UK. Such knowledge is critical for developing more robust biosecurity and surveillance plans and better preparing Scotland against incursions of emerging swine diseases.

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“…Complex network is a useful tool to analyze the impact of risk spread on supply chain [6][7][8][9][10]. There has been increased attention focused on supply chain risk from various directions.…”

Section: Introductionmentioning

confidence: 99%

“…Monique A. van der Gaaga Fred Vosa et al [12] design a detailed stochastic state transition model and analyze its characteristics. Kathrin Buttnera et al [13] present a static poultry trade supply chain network to analyze its statistic characteristics. In this paper, we mainly study on construction of supply chain risk network based on complex network theory.…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction of Supply Chain Network Based on Fixed Spread Risk

Wen¹

2013

SJAMS

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Abstract:Supply chain is an integrated network where suppliers, sellers and customers are highly interconnected through business activities. The basic character of supply chain is complexity, dynamic and growth. In this paper, based on the complex network theory, we build a supply chain network model based on gradual risk. The dynamic growth mechanism of the supply chain network is based on the local world evolving model. The new node adds the local world entering with a certain probability. Combined with supply chain, the node, the edge and the weighted and the network are being redefined. The process of how new added node assign risk to old nodes is also introduced

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Static network analysis of a pork supply chain in Northern Germany—Characterisation of the potential spread of infectious diseases via animal movements

Cited by 75 publications

References 16 publications

Simulation of between-farm transmission of porcine reproductive and respiratory syndrome virus in Ontario, Canada using the North American Animal Disease Spread Model

Simulation of between-farm transmission of porcine reproductive and respiratory syndrome virus in Ontario, Canada using the North American Animal Disease Spread Model

How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis

Research on the Construction of Supply Chain Network Based on Fixed Spread Risk

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