Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread

Natale, Franca; Giovannini, Armando; Savini, Lara; Palma, Diana; Possenti, Luigi; Fiore, Gianluca; Calistri, Paolo

doi:10.1016/j.prevetmed.2009.08.026

Cited by 173 publications

(218 citation statements)

References 23 publications

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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%

“…Traceability becomes a tool that may be used to carry out surveillance, prevention or control of animal disease (Ammendrup and Barcos, 2006). 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: Introductionmentioning

confidence: 99%

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

Rautureau

Dufour

Durand

2012

Animal

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“…Among the adopted strategies, data obtained from the beef cattle farm together with the information registered on the Animal Movement Permits (AMPs) showing where the cattle were last located and to where it was destined, allowed us to construct a movement network and to observe a high influx of animals ( Fig.1) towards node #1 (studied area). Furthermore, in terms of a disease spread, the network diameter is the maximum number of edges that should exist, so that a disease could reach the linear size of a network (Natale et al 2009). Hence, for the studied network, animals infected with bovine cysticercosis reached the furthermost livestock holding whenever there were at least five issued AMPs for those animals.…”

Section: Discussionmentioning

confidence: 99%

“…Networks offer both a visual and mathematical interpretation to data, capturing and describing the structure of interactions (Keeling & Eames 2005, Keeling et al 2010. Examples of network analysis applied to cattle movement data include network--based epidemiological model for detection of a disease outbreak (Reis et al 2007) , assessment of pathogen dynamics on a spatiotemporal structure (Kao et al 2006), epidemiological models of Foot and Mouth disease spread in Great Britain (Green et al 2006) , risk evaluation for potential disease spread (Natale et al 2009, Ribeiro-Lima et al 2015, and infectious disease surveillance (Amaku et al 2015).This study aimed at testing the utility of an animal movement network constructed with data from a farm that acquires cattle from several other different farms to map the major contributors of cysticercosis propagation. Based on the results of the network analysis, control measures were applied to decrease cysticercosis' occurrence in the studied farm.…”

Section: Introductionmentioning

confidence: 99%

Animal movement network analysis as a tool to map farms serving as contamination source in cattle cysticercosis

et al. 2017

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Bovine cysticercosis is a problem distributed worldwide that result in economic losses mainly due to the condemnation of infected carcasses. One of the difficulties in applying control measures is the identification of the source of infection, especially because cattle are typically acquired from multiple farms. Here, we tested the utility of an animal movement network constructed with data from a farm that acquires cattle from several other different farms to map the major contributors of cysticercosis propagation. Additionally, based on the results of the network analysis, we deployed a sanitary management and drug treatment scheme to decrease cysticercosis’ occurrence in the farm. Six farms that had commercial trades were identified by the animal movement network and characterized as the main contributors to the occurrence of cysticercosis in the studied farm. The identification of farms with a putative risk of Taenia saginata infection using the animal movement network along with the proper sanitary management and drug treatment resulted in a gradual decrease in cysticercosis prevalence, from 25% in 2010 to 3.7% in 2011 and 1.8% in 2012. These results suggest that the animal movement network can contribute towards controlling bovine cysticercosis, thus minimizing economic losses and preventing human taeniasis.

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“…Real-world plant trade networks are neither static in their structure nor uniform in the strength of the connections. The relatively rare use of network epidemiology in botanical science compared with what has happened in human and animal pathology (13,58,89) may be due to lack of suitable data for plant diseases, but could well change in the future with the adoption of the latest genetic technologies also in plant sciences. Examples of future challenges for a successful use of network theory in plant disease epidemiology include the following.…”

Section: Future Challenges In Plant Network Epidemiologymentioning

confidence: 99%

Networks in Plant Epidemiology: From Genes to Landscapes, Countries, and Continents

Moslonka‐Lefebvre¹,

Finley²,

Dorigatti³

et al. 2011

Phytopathology®

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Networks are sets of nodes connected by links in various ways (Box 1). Although the properties of random networks have already been systematically investigated in the 1960s, a growing body of literature is now using networks in a range of ecological applications, including the study and management of human, animal, and plant diseases (29,49,58,80,131) (Fig. 1). Given the generality and flexibility of the approach, network representations can be used at a variety of levels in plant pathology, from gene expression during host-pathogen interactions, to the development of plant epidemics among fields, farms, and landscapes and to trade movement of plants infected by pathogens or infested by insects among regions and countries.Network structure has profound effects on the dynamics of an epidemic within a population (51,61,127). In today's globally connected world, social and transportation networks play a crucial role in the spread of human infectious diseases (21,53,83). A network approach provides insights into the transmission of infectious diseases also in animals more generally (40,45,78,137). Although there is an increasing interdisciplinary application of networks in epidemiology, relatively little attention has been paid to these analytical approaches in plant sciences. Hence the need for this review, which aims to summarize recent progress in this rapidly developing field and to highlight research challenges specific to plant pathology.In today's plant pathology, as in other fields, there is a need for integrating investigations at the molecular, mycelium, plant, regional and international scale (9,48,102,111,118,120,132). Networks can provide such a unifying framework. They can be (i) perceived at an abstract level (e.g., fungal species occurring on the same plant species host), (ii) materialized by a physical structure (e.g., the root system of plant individuals connected by mycorrhiza, or vice versa), and (iii) underlying flows of energy, matter or information (e.g., the exchanges of knowledge, equipment and money among farmers, plant health consultants, researchers and phytopharmaceutical companies). There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of superconnected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pat...

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Network analysis of Italian cattle trade patterns and evaluation of risks for potential disease spread

Cited by 173 publications

References 23 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

Animal movement network analysis as a tool to map farms serving as contamination source in cattle cysticercosis

Networks in Plant Epidemiology: From Genes to Landscapes, Countries, and Continents

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