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

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 scale-free 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.

[1]  Beatriz Martínez-López,et al.  Prediction of Pig Trade Movements in Different European Production Systems Using Exponential Random Graph Models , 2017, Front. Vet. Sci..

[2]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[3]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[4]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[5]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[6]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[7]  Fernando Ferreira,et al.  Modeling the Dynamics of Infectious Diseases in Different Scale-Free Networks with the Same Degree Distribution , 2013, ArXiv.

[8]  T. Carpenter,et al.  Evaluation of control and surveillance strategies for classical swine fever using a simulation model. , 2013, Preventive veterinary medicine.

[9]  Joachim Krieter,et al.  Static network analysis of a pork supply chain in Northern Germany-Characterisation of the potential spread of infectious diseases via animal movements. , 2013, Preventive veterinary medicine.

[10]  A. Gogin,et al.  African swine fever in the Russian Federation: risk factors for Europe and beyond , 2013 .

[11]  Y. Becker.  Epidemiology of African Swine Fever Virus , 1987 .

[12]  I. Kiss,et al.  Infectious disease control using contact tracing in random and scale-free networks , 2006, Journal of The Royal Society Interface.

[13]  F. Casabianca,et al.  Multivariate analysis of traditional pig management practices and their potential impact on the spread of infectious diseases in Corsica. , 2015, Preventive veterinary medicine.

[14]  C W Revie,et al.  Analysis of Swine Movement in Four Canadian Regions: Network Structure and Implications for Disease Spread. , 2016, Transboundary and emerging diseases.

[15]  Benjamin Ivorra,et al.  Evaluation of the risk of classical swine fever (CSF) spread from backyard pigs to other domestic pigs by using the spatial stochastic disease spread model Be-FAST: the example of Bulgaria. , 2013, Veterinary microbiology.

[16]  Mark D. F. Shirley,et al.  The impacts of network topology on disease spread , 2005 .

[17]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[18]  M. Everett,et al.  Recent network evolution increases the potential for large epidemics in the British cattle population , 2007, Journal of The Royal Society Interface.

[19]  B. Martínez-López,et al.  Combined application of social network and cluster detection analyses for temporal-spatial characterization of animal movements in Salamanca, Spain. , 2009, Preventive Veterinary Medicine.

[20]  B. Martínez-López,et al.  A novel spatial and stochastic model to evaluate the within- and between-farm transmission of classical swine fever virus. I. General concepts and description of the model. , 2011, Veterinary microbiology.

[21]  M. Konschake,et al.  Trade communities and their spatial patterns in the German pork production network. , 2011, Preventive veterinary medicine.

[22]  C. Lanzas,et al.  Complex system modelling for veterinary epidemiology. , 2015, Preventive veterinary medicine.

[23]  M. Amaku,et al.  Detecting livestock production zones. , 2013, Preventive veterinary medicine.

[24]  Teresa Rabade,et al.  Pig farming in the European Union: considerable variations from one Member State to another , 2017 .

[25]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[26]  E. Engvall,et al.  Leptospira seroprevalence and associations between seropositivity, clinical disease and host factors in horses , 2009, Acta veterinaria Scandinavica.

[27]  M. G. Garner,et al.  Modelling the spread of foot-and-mouth disease in Australia. , 2005, Australian veterinary journal.

[28]  M M Telo da Gama,et al.  Recurrent epidemics in small world networks. , 2004, Journal of theoretical biology.

[29]  M. Nöremark,et al.  Spatial and temporal investigations of reported movements, births and deaths of cattle and pigs in Sweden , 2009, Acta veterinaria Scandinavica.

[30]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[31]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[32]  S. Riley.  Large-Scale Spatial-Transmission Models of Infectious Disease , 2007, Science.

[33]  B. Durand,et al.  Structural vulnerability of the French swine industry trade network to the spread of infectious diseases. , 2012, Animal : an international journal of animal bioscience.

[34]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[35]  G. Medley,et al.  Mathematical modelling of the foot and mouth disease epidemic of 2001: strengths and weaknesses. , 2002, Research in veterinary science.

[36]  M. Nöremark,et al.  Network analysis of cattle and pig movements in Sweden: measures relevant for disease control and risk based surveillance. , 2011, Preventive veterinary medicine.

[37]  R. Christley,et al.  Descriptive and social network analysis of pig transport data recorded by quality assured pig farms in the UK. , 2013, Preventive veterinary medicine.

[38]  M. Keeling,et al.  Modelling foot-and-mouth disease: a comparison between the UK and Denmark. , 2008, Preventive veterinary medicine.

[39]  M. Woolhouse,et al.  How commercial and non-commercial swine producers move pigs in Scotland: a detailed descriptive analysis , 2014, BMC Veterinary Research.

[40]  L J Frewer,et al.  Defining European preparedness and research needs regarding emerging infectious animal diseases: results from a Delphi expert consultation. , 2012, Preventive veterinary medicine.

[41]  L. Danon,et al.  Demographic structure and pathogen dynamics on the network of livestock movements in Great Britain , 2006, Proceedings of the Royal Society B: Biological Sciences.

[42]  R. Christley,et al.  Exploring the role of auction markets in cattle movements within Great Britain. , 2007, Preventive veterinary medicine.

[43]  M. Konschake,et al.  On the Robustness of In- and Out-Components in a Temporal Network , 2013, PloS one.

[44]  B. Martínez-López,et al.  Social network analysis. Review of general concepts and use in preventive veterinary medicine. , 2009, Transboundary and emerging diseases.

[45]  B. Ivorra,et al.  A novel spatial and stochastic model to evaluate the within and between farm transmission of classical swine fever virus: II validation of the model. , 2012, Veterinary microbiology.

[46]  J. Nunes,et al.  Agris category code : L 01 , P 01 INVENTORY AND CHARACTERIZATION OF TRADITIONAL MEDITERRANEAN PIG PRODUCTION SYSTEMS . ADVANTAGES AND CONSTRAINTS TOWARDS ITS DEVELOPMENT , 2013 .

[47]  D. Pfeiffer,et al.  Multivariate analysis of management and biosecurity practices in smallholder pig farms in Madagascar , 2009, Preventive veterinary medicine.

[48]  C. Staubach,et al.  Epidemiology of classical swine fever in Germany in the 1990s. , 2000, Veterinary microbiology.

[49]  R S Morris,et al.  InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations. , 2013, Preventive veterinary medicine.

[50]  R. Huirne,et al.  The risk of the introduction of classical swine fever virus at regional level in the European Union: a conceptual framework. , 2003, Revue scientifique et technique.

[51]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[52]  Matthias Greiner,et al.  Relationship of trade patterns of the Danish swine industry animal movements network to potential disease spread. , 2007, Preventive veterinary medicine.

[53]  Z. Poljak,et al.  Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management. , 2013, Preventive veterinary medicine.

[54]  L. Danon,et al.  The role of routine versus random movements on the spread of disease in Great Britain , 2009, Epidemics.

[55]  D. Pfeiffer,et al.  Stochastic spatio-temporal modelling of African swine fever spread in the European Union during the high risk period. , 2013, Preventive veterinary medicine.