Dynamic contact networks of swine movement in Manitoba, Canada: Characterization and implications for infectious disease spread

Augusta, Carolyn; Taylor, Graham W.; Deardon, Rob

doi:10.1111/tbed.13220

Cited by 7 publications

(11 citation statements)

References 28 publications

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“…The role of haulage has been previously identified as an important route of transmission (Dee, Deen, Otake, & Pijoan, ), leading to multiple studies evaluating the impact of sharing haulage vehicles on livestock trade networks (Augusta, Taylor, & Deardon, ; Bernini et al, ; Salines et al, ; Thakur et al, ). Although Bernini and colleagues highlighted the importance of considering indirect contacts when estimating the final size of infectious disease epidemics in livestock at regional level, little has been done to fully explore and quantify the associated risk of infectious disease spread at national level.…”

Section: Discussionmentioning

confidence: 99%

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Porphyre

Bronsvoort

Gunn

et al. 2020

Transbound Emerg Dis

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When assessing the role of live animal trade networks in the spread of infectious diseases in livestock, attention has focused mainly on direct movements of animals between premises, whereas the role of haulage vehicles used during transport, an indirect route for disease transmission, has largely been ignored. Here, we have assessed the impact of sharing haulage vehicles from livestock transport service providers on the connectivity between farms as well as on the spread of swine infectious diseases in Great Britain (GB). Using all pig movement records between April 2012 and March 2014 in GB, we built a series of directed and weighted static multiplex networks consisting of two layers of identical nodes, where nodes (farms) are linked either by (a) the direct movement of pigs and (b) the shared use of haulage vehicles. The haulage contact definition integrates the date of the move and the duration Δs that lorries are left contaminated by pathogens, hence accounting for the temporal aspect of contact events. For increasing Δs, descriptive network analyses were performed to assess the role of haulage on network connectivity. We then explored how viruses may spread throughout the GB pig sector by computing the reproduction number R. Our results showed that sharing haulage vehicles increases the number of contacts between farms by >50% and represents an important driver of disease transmission. In particular, sharing haulage vehicles, even if Δs < 1 day, will limit the benefit of the standstill regulation, increase the number of premises that could be infected in an outbreak, and more easily raise R above 1. This work confirms that sharing haulage vehicles has significant potential for spreading infectious diseases within the pig sector. The cleansing and disinfection process of haulage vehicles is therefore a critical control point for disease transmission risk mitigation.

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Section: Discussionmentioning

confidence: 99%

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Porphyre

Bronsvoort

Gunn

et al. 2020

Transbound Emerg Dis

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“…Most published approaches make use of static networks to describe animal movement and subsequently make inferences about outbreaks and epidemics (Büttner et al, 2013(Büttner et al, , 2018Marquetoux et al, 2016;Motta et al, 2017;Alarcón et al, 2019;Kinsley et al, 2019). However, the temporal anomalies and seasonality of movements can only be identified if movement paths are aggregated to smaller time windows (Cárdenas et al, 2018;Augusta et al, 2019;Sterchi et al, 2019), which may not be sufficient to make inferences about disease dynamics within the contact networks.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Machado¹,

Galvis²,

Lopes

et al. 2020

Transbound Emerg Dis

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Tracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control .

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“…However, due to the fact that other pig trade networks focusing on direct animal movements showed similar characteristics (e.g., pyramidal acyclic and directed structure, right-skewed distribution of centrality parameters, heterogeneous contact patterns) [ 4 , 5 , 17 , 49 , 50 , 51 ], the present results contribute to a great extent to a further understanding of the spreading processes within animal trade networks. Particularly the integration of other transmission routes as performed in the present study into simulation models of the spreading processes within the network based on contact structures is to the authors’ knowledge performed in only a low number of studies for animal trade networks [ 5 , 11 , 13 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, the crude assumptions made for the epidemiological model may lead to an underestimation of the final epidemic size.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the fact that the inclusion of different transmission routes offers the more detailed illustration of the real probability of the disease spread, more recent studies also included indirect transmission routes into the network analysis and evaluated their impact on potential disease spread [ 5 , 11 , 13 , 18 , 19 , 21 , 22 , 23 , 24 , 25 ]. However, most of these studies focused on the inclusion of indirect disease transmission via livestock trucks.…”

Section: Introductionmentioning

confidence: 99%

Illustration of Different Disease Transmission Routes in a Pig Trade Network by Monopartite and Bipartite Representation

Büttner¹,

Krieter²

2020

Animals

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Besides the direct transport of animals, also indirect transmission routes, e.g., contact via contaminated vehicles, have to be considered. In this study, the transmission routes of a German pig trade network were illustrated as a monopartite animal movements network and two bipartite networks including information of the transport company and the feed producer which were projected on farm level (n = 866) to enable a comparison. The networks were investigated with the help of network analysis and formed the basis for epidemiological models to evaluate the impact of different transmission routes on network structure as well as on potential epidemic sizes. The number of edges increased immensely from the monopartite animal movements network to both projected networks. The median centrality parameters revealed clear differences between the three representations. Furthermore, moderate correlation coefficients ranging from 0.55 to 0.68 between the centrality values of the animal movements network and the projected transportation network were obtained. The epidemiological models revealed significantly more infected farms for both projected networks (70% to 100%) compared to the animal movements network (1%). The inclusion of indirect transmission routes had an immense impact on the outcome of centrality parameters as well as on the results of the epidemiological models.

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Dynamic contact networks of swine movement in Manitoba, Canada: Characterization and implications for infectious disease spread

Cited by 7 publications

References 28 publications

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Multilayer network analysis unravels haulage vehicles as a hidden threat to the British swine industry

Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

Illustration of Different Disease Transmission Routes in a Pig Trade Network by Monopartite and Bipartite Representation

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