Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread

Bigras-Poulin, Michel; Barfod, Kristen; Mortensen, Sten; Greiner, Matthias

doi:10.1016/j.prevetmed.2007.02.004

Cited by 108 publications

(138 citation statements)

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“…Heterogeneity in the distribution of links within a network is a key factor and reveals the presence of central individuals (hubs) that are the most likely to spread a disease and that could be targeted by control measures. The French swine trade network had the same topology as animal trade networks described elsewhere in Europe (Christley et al, 2005;Bigras-Poulin et al, 2006;Kiss et al, 2006b;Bigras-Poulin et al, 2007;Natale et al, 2009;Volkova et al, 2010a and2010b).…”

Section: Discussionmentioning

confidence: 99%

“…The characteristics of the French monthly swine trade network are similar to those of the Swedish monthly swine trade network (Nö remark et al, 2011; density, clustering coefficient and assortativity). The Danish swine industry network was described as being strongly heterogeneous within and between farms (Bigras-Poulin et al, 2007). Nö remark et al (2011) also found associations between in-degree or ingoing infection chain centrality and production type by classifying the herds according to production type 'highest in the pyramid'.…”

Section: Discussionmentioning

confidence: 99%

“…For example, national cattle trade data have been widely studied using network analysis methods (social network analysis (SNA)) for the characterization of trade organization and the detection of communities to qualify their vulnerability structure and to target surveillance (Christley et al, 2005;Bigras-Poulin et al, 2006;Natale et al, 2009;Rautureau et al, 2011;Nö remark et al, 2011). Fewer studies -E-mail: severine.rautureau@anses.fr; sev_rautureau@yahoo.fr were dedicated to the pig trade network (Bigras-Poulin et al, 2007;Martinez-Lopez et al, 2009a;Lentz et al, 2011;Nö remark et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Rautureau

Dufour

Durand

2012

Animal

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The networks generated by live animal movements are the principal vector for the propagation of infectious agents between farms, and their topology strongly affects how fast a disease may spread. The structural characteristics of networks may thus provide indicators of network vulnerability to the spread of infectious disease. This study applied social network analysis methods to describe the French swine trade network. Initial analysis involved calculating several parameters to characterize networks and then identifying high-risk subgroups of holdings for different time scales. Holding-specific centrality measurements (‘degree’, ‘betweenness’ and ‘ingoing infection chain’), which summarize the place and the role of holdings in the network, were compared according to the production type. In addition, network components and communities, areas where connectedness is particularly high and could influence the speed and the extent of a disease, were identified and analysed. Dealer holdings stood out because of their high centrality values suggesting that these holdings may control the flow of animals in part of the network. Herds with growing units had higher values for degree and betweenness centrality, representing central positions for both spreading and receiving disease, whereas herds with finishing units had higher values for in-degree and ingoing infection chain centrality values and appeared more vulnerable with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases. This reflects the dynamics of the swine trade with downward movements along the production chain. But, the significant heterogeneity of farms with several production units did not reveal any particular type of production for targeting disease surveillance or control. Besides, no giant strong connected component was observed, the network being rather organized according to communities of small or medium size (<20% of network size). Because of this fragmentation, the swine trade network appeared less structurally vulnerable than ruminant trade networks. This fragmentation is explained by the hierarchical structure, which thus limits the structural vulnerability of the global trade network. However, inside communities, the hierarchical structure of the swine production system would favour the spread of an infectious agent (especially if introduced in breeding herds).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Rautureau

Dufour

Durand

2012

Animal

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“…Studies have cited that animal traffic is an important risk factor for disease dissemination (BIGRAS-POULIN et al, 2007;CAPANEMA et al, 2012). This raises the possibility of CL transmission through movement and close contact between the infection sources, susceptible vectors, and interferences at the agglomeration site, as well as the traffic between region's goat and sheep breeding farms to the animal fairs.…”

Section: Discussionmentioning

confidence: 99%

Seroprevalence of caseous lymphadenitis in goats sold in an animal fair in the northeastern semi-arid region of Brazil

Alves

Farias

Lima³

et al. 2018

SCA

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Goat farming in the northeastern regions of Brazil plays an important role in the socio-economic functions of the country. However, high rates of morbidity and mortality occur in the animals, caused primarily by infectious diseases like caseous lymphadenitis, which is widespread in goat herds; this causes serious economic losses to the farming business. Although events such as animal fairs are common in the region, wherein most of the goats and sheep are sold, the risk of disease transmission remains, since the health condition of the animal is unknown. The aim of this study was to determine the frequency of anti-Corynebacterium pseudotuberculosis antibodies present in the goats sold at the animal fair of Tabira -PE, Northeastern semi-arid. Serum samples from 233 goats were collected from the period of November 2014 to June 2015. The diagnosis of infection by C. pseudotuberculosis was made using the indirect ELISA technique. The number of goats seropositive for C. pseudotuberculosis was identified as 87 (37.34%; 95% CI = 31.38 -43.71%). Of the nine flocks evaluated, five (55.55%) were positive. Thus, it is suggested that infection by C. pseudotuberculosis is widespread in the goats sold at the animal fairs in the semi-arid Northeastern, reinforcing the need for disease diagnosis in goat herds in the region for the possible implementation of disease control programs. Key words: Agglomerations. Caseous lymphadenitis. Goats. Serology. ResumoA caprinocultura se destaca na região Nordeste do Brasil por desempenhar importante função socioeconômica. Contudo altas taxas de morbidade e mortalidade ocorrem nas propriedades causadas principalmente por doenças infecciosas, destacando-se a linfadenite caseosa, que se encontra amplamente difundida nos rebanhos caprinos, acarretando sérios prejuízos econômicos à atividade. Eventos do tipo

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“…However, epidemics are more likely to start and to become widespread during a period of high movement activity , which was illustrated during the FMD epidemic in the UK in 2001(Gibbens et al 2001. Moreover, studies in cattle (Bigras-Poulin et al 2006, Natale et al 2009) and pigs (Bigras-Poulin et al 2007, Lindstrom et al 2010 showed differences in the contact structure across different production phases, which are likely to affect the course of an epidemic. This suggests that there is value 70 Werkman et al: Live fish movements between farms in studying aquaculture network structures in more detail.…”

Section: Resale or Republication Not Permitted Without Written Consenmentioning

confidence: 99%

Seasonality and heterogeneity of live fish movements in Scottish fish farms

Werkman¹,

Green²,

Munro³

et al. 2011

Dis. Aquat. Org.

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Movement of live animals is a key contributor to disease spread. Farmed Atlantic salmon Salmo salar, rainbow trout Onchorynchus mykiss and brown/sea trout Salmo trutta are initially raised in freshwater (FW) farms; all the salmon and some of the trout are subsequently moved to seawater (SW) farms. Frequently, fish are moved between farms during their FW stage and sometimes during their SW stage. Seasonality and differences in contact patterns across production phases have been shown to influence the course of an epidemic in livestock; however, these parameters have not been included in previous network models studying disease transmission in salmonids. In Scotland, farmers are required to register fish movements onto and off their farms; these records were used in the present study to investigate seasonality and heterogeneity of movements for each production phase separately for farmed salmon, rainbow trout and brown/sea trout. Salmon FW-FW and FW-SW movements showed a higher degree of heterogeneity in number of contacts and different seasonal patterns compared with SW-SW movements. FW-FW movements peaked from May to July and FW-SW movements peaked from March to April and from October to November. Salmon SW-SW movements occurred more consistently over the year and showed fewer connections and number of repeated connections between farms. Therefore, the salmon SW-SW network might be treated as homogeneous regarding the number of connections between farms and without seasonality. However, seasonality and production phase should be included in simulation models concerning FW-FW and FW-SW movements specifically. The number of rainbow trout FW-FW and brown/sea trout FW-FW movements were different from random. However, movements from other production phases were too low to discern a seasonal pattern or differences in contact pattern. KEY WORDS: Disease transmission · Epidemiology · Contact structure · Aquaculture Resale or republication not permitted without written consent of the publisherDis Aquat Org 96: [69][70][71][72][73][74][75][76][77][78][79][80][81][82] 2011 (hatcheries) to the bottom (smolt producers or ongrowers), which can be compared with the movement structure of industries such as of pigs (Lindstrom et al. 2010) and poultry (Cox & Pavic 2010).Live fish movements are a risk for pathogen transmission between farms (Murray et al. 2002, Murray & Peeler 2005. Pathogens can also be introduced by other pathways such as well-boat visits (Murray et al. 2002) and on a local level by water movement (Jonkers et al. 2010) or by wild fish movements (Uglem et al. 2009). Disease outbreaks can cause reduced appetite, reduced growth and increased mortality rates, depending on the disease (OIE 2009), reducing production and profitability (Murray & Peeler 2005). In addition, disease outbreaks can cause welfare problems (Turnbull & Kadri 2007), and pathogen accumulation in fish farms may lead to transmission of pathogens to wild fish populations (Wallace et al. 2008).If fish are infected and transported there ...

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Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread

Cited by 108 publications

References 14 publications

Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Structural vulnerability of the French swine industry trade network to the spread of infectious diseases

Seroprevalence of caseous lymphadenitis in goats sold in an animal fair in the northeastern semi-arid region of Brazil

Seasonality and heterogeneity of live fish movements in Scottish fish farms

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