Besides farming, trade of livestock is a major component of agricultural economy. However, the networks generated by live animal movements are the major support for the propagation of infectious agents between farms, and their structure strongly affects how fast a disease may spread. Structural characteristics may thus be indicators of network vulnerability to the spread of infectious disease. The method proposed here is based upon the analysis of specific subnetworks: the giant strongly connected components (GSCs). Their existence, size and geographic extent are used to assess network vulnerability. Their disappearance when targeted nodes are removed allows studying how network vulnerability may be controlled under emergency conditions. The method was applied to the cattle trade network in France, 2005. Giant strongly connected components were present and widely spread all over the country in yearly, monthly and weekly networks. Among several tested approaches, the most efficient way to make GSCs disappear was based on the ranking of nodes by decreasing betweenness centrality (the proportion of shortest paths between nodes on which a specific node lies). Giant strongly connected components disappearance was obtained after removal of <1% of network nodes. Under emergency conditions, suspending animal trade activities in a small subset of holdings may thus allow to control the spread of an infectious disease through the animal trade network. Nodes representing markets and dealers were widely affected by these simulated control measures. This confirms their importance as 'hubs' for infectious diseases spread. Besides emergency conditions, specific sensitization and preventive measures should be dedicated to this population.
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).
Knowledge on host-feeding pattern of blood-sucking insects helps to understand the epidemiology of a vector-born disease. We determined blood meal origin from blood-fed Culicoides thanks to molecular techniques. A set of primers was used to selectively amplify segment of vertebrates' prepronociceptin gene from abdomen of engorged Culicoides (Diptera: Ceratopogonidae). Vertebrate DNA was successfully amplified in 91% of blood-fed Culicoides assayed. Direct sequencing and comparison of resultant sequences with sequences in GenBank, using BLAST, lead to the specific identification of the host in 100% of the cases. A total of 157 blood-fed females belonging to 13 different Culicoides' species were captured thanks to light traps in different areas of France between 2008 and 2009. Blood meal origin was determined for 143 blood-fed midges: 59 Culicoides obsoletus, 18 Culicoides dewulfi, 16 Culicoides scoticus, 11 Culicoides chiopterus, 10 Culicoides lupicaris, 1 Culicoides pulicaris, 8 Culicoides punctatus, 10 Culicoides pallidicornis, 3 Culicoides achrayi, 2 Culicoides furcillatus, 3 Culicoides brunnicans, 1 Culicoides picturatus and 1 Culicoides poperinghensis. The predominant species in our study belong to the C. obsoletus complex; they are considered as putative vectors of Bluetongue virus in the north of Europe. C. chiopterus sampled fed only on cattle, while blood meal origin of C. dewulfi, C. obsoletus and C. scoticus was diversified. In our sampling, we found that midges were fed mainly on cattle (54%), rabbits (20%), horses (17%), sheep (4%), pigs or wild boars (4%) and human (1%). Cattle DNA was found in at least 11 different species of Culicoides assayed.
The purpose of this study was to develop a standardized tool for the assessment of surveillance systems on zoonoses and animal diseases. We reviewed three existing methods and combined them to develop a semi-quantitative assessment tool associating their strengths and providing a standardized way to display multilevel results. We developed a set of 78 assessment criteria divided into ten sections, representing the functional parts of a surveillance system. Each criterion was given a score according to the prescription of a scoring guide. Three graphical assessment outputs were generated using a specific combination of the scores. Output 1 is a general overview through a series of pie charts synthesizing the scores of each section. Output 2 is a histogram representing the quality of eight critical control points. Output 3 is a radar chart representing the level reached by ten system attributes. This tool was applied on five surveillance networks.
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