Spatial and Functional Organization of Pig Trade in Different European Production Systems: Implication for Disease Prevention and Control

被引:40
作者
Relun, Anne [1 ,2 ]
Grosbois, Vladimir [1 ]
Manuel Sanchez-Vizcaino, Jose [3 ]
Alexandrov, Tsviatko [4 ]
Feliziani, Francesco [5 ]
Waret-Szkuta, Agnes [6 ]
Molia, Sophie [1 ]
Etter, Eric Marcel Charles [1 ,7 ]
Martinez-Lopez, Beatriz [2 ]
机构
[1] Ctr Cooperat Int Rech Agron Dev CIRAD, UPR Anim & Integrated Risk Management AGIRs, Montpellier, France
[2] Univ Calif Davis, Sch Vet Med, Dept Med & Epidemiol, CADMS, Davis, CA 95616 USA
[3] Univ Complutense Madrid, Anim Hlth Dept, Madrid, Spain
[4] Bulgarian Food Safety Agcy, Sofia, Bulgaria
[5] Ist Zooprofilatt Sperimentale Umbria & March, Perugia, Italy
[6] ENVT, INP, Toulouse, France
[7] Univ Pretoria, Fac Vet Sci, Dept Prod Anim Studies, Epidemiol Sect, Onderstepoort, South Africa
来源
FRONTIERS IN VETERINARY SCIENCE | 2016年 / 3卷
关键词
network analysis; community; movements; risk-based surveillance; swine; infectious diseases;
D O I
10.3389/fvets.2016.00004
中图分类号
S85 [动物医学(兽医学)];
学科分类号
0906 ;
摘要
Understanding the complexity of live pig trade organization is a key factor to predict and control major infectious diseases, such as classical swine fever (CSF) or African swine fever (ASF). Whereas the organization of pig trade has been described in several European countries with indoor commercial production systems, little information is available on this organization in other systems, such as outdoor or small-scale systems. The objective of this study was to describe and compare the spatial and functional organization of live pig trade in different European countries and different production systems. Data on premise characteristics and pig movements between premises were collected during 2011 from Bulgaria, France, Italy, and Spain, which swine industry is representative of most of the production systems in Europe (i.e., commercial vs. small-scale and outdoor vs. indoor). Trade communities were identified in each country using the Walktrap algorithm. Several descriptive and network metrics were generated at country and community levels. Pig trade organization showed heterogeneous spatial and functional organization. Trade communities mostly composed of indoor commercial premises were identified in western France, northern Italy, northern Spain, and north-western Bulgaria. They covered large distances, overlapped in space, demonstrated both scale-free and small-world properties, with a role of trade operators and multipliers as key premises. Trade communities involving outdoor commercial premises were identified in western Spain, south-western and central France. They were more spatially clustered, demonstrated scalefree properties, with multipliers as key premises. Small-scale communities involved the majority of premises in Bulgaria and in central and Southern Italy. They were spatially clustered and had scale-free properties, with key premises usually being commercial production premises. These results indicate that a disease might spread very differently according to the production system and that key premises could be targeted to more cost-effectively control diseases. This study provides useful epidemiological information and parameters that could be used to design risk-based surveillance strategies or to more accurately model the risk of introduction or spread of devastating swine diseases, such as ASF, CSF, or foot-and-mouth disease.
引用
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页数:12
相关论文
共 56 条
[1]   Statistical mechanics of complex networks [J].
Albert, R ;
Barabási, AL .
REVIEWS OF MODERN PHYSICS, 2002, 74 (01) :47-97
[2]  
Alexandrov T, 2011, REV EPID SAN ANIM, V59-60, P140
[3]   Multiscale mobility networks and the spatial spreading of infectious diseases [J].
Balcan, Duygu ;
Colizza, Vittoria ;
Goncalves, Bruno ;
Hu, Hao ;
Ramasco, Jose J. ;
Vespignani, Alessandro .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2009, 106 (51) :21484-21489
[4]   Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread [J].
Bigras-Poulin, Michel ;
Barfod, Kristen ;
Mortensen, Sten ;
Greiner, Matthias .
PREVENTIVE VETERINARY MEDICINE, 2007, 80 (2-3) :143-165
[5]   Static network analysis of a pork supply chain in Northern Germany-Characterisation of the potential spread of infectious diseases via animal movements [J].
Buettner, Kathrin ;
Krieter, Joachim ;
Traulsen, Arne ;
Traulsen, Imke .
PREVENTIVE VETERINARY MEDICINE, 2013, 110 (3-4) :418-428
[6]   Power-Law Distributions in Empirical Data [J].
Clauset, Aaron ;
Shalizi, Cosma Rohilla ;
Newman, M. E. J. .
SIAM REVIEW, 2009, 51 (04) :661-703
[7]   Epidemiology of African swine fever virus [J].
Costard, S. ;
Mur, L. ;
Lubroth, J. ;
Sanchez-Vizcaino, J. M. ;
Pfeiffer, D. U. .
VIRUS RESEARCH, 2013, 173 (01) :191-197
[8]   Multivariate analysis of management and biosecurity practices in smallholder pig farms in Madagascar [J].
Costard, S. ;
Porphyre, V. ;
Messad, S. ;
Rakotondrahanta, S. ;
Vidon, H. ;
Roger, F. ;
Pfeiffer, D. U. .
PREVENTIVE VETERINARY MEDICINE, 2009, 92 (03) :199-209
[9]  
Cozzi Giulio, 2003, Agriculturae Conspectus Scientificus, V68, P71
[10]  
Csardi G., 2006, Inter Journal, Complex Systems, P1695