Small- and large-scale network structure of live fish movements in Scotland

Green, Darren M.; Gregory, A; Munro, L A

doi:10.1016/j.prevetmed.2009.05.031

Cited by 48 publications

(56 citation statements)

References 31 publications

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“…Inter-site contact can be represented by a network, where the direction and strength of potentially infectious contact by mechanisms such as fish movements is explicitly modelled between individual pairs of farms. This has been developed for UK aquaculture industries by Thrush and Peeler (2006), Munro and Gregory (2009) and Green et al (2009). Network spread tends to reduce the effective distance between farms compared to local spread, due to long-distance connections producing 'small world'-like network dynamics (Kao et al, 2006).…”

Section: Model Behaviour and Assumptionsmentioning

confidence: 99%

“…Aside from the complication of the delay between shedding of infectious agent on one site, and its arrival at another site, the weighting and directionality of such potentially infectious contact, as predicted by advection models, could again be incorporated into existing contact network models for disease transmission. Moreover, anthropogenic spread of disease through, for example, live fish movements can also be incorporated into a network model framework (inter alia Green et al, 2009), and is a natural extension to the strategic model presented here.…”

Section: Practical Application Of the Modelmentioning

confidence: 99%

“…Wellboat movements have been implicated as a potential disease transmission risk (Murray et al, 2002); and for ISA, one recent study indicated risk from sources aside from the local contact network and seaway to be a large component of risk of infection in the Norwegian salmon industry (Scheel et al, 2007). Though an important route of pathogen introduction, for salmon, site-to-site movements outside of stocking and and harvest are relatively infrequent (Green et al, 2009). Therefore, strategically, there is merit in considering the potential spread of epidemic diseases that operate over short timescales through local areas such as single management areas, outside of introduction through anthropogenic routes.…”

Section: Introductionmentioning

confidence: 99%

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A strategic model for epidemic control in aquaculture

Green

2010

Preventive Veterinary Medicine

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A stochastic metapopulation model of infectious disease was developed to model the spread of disease within and between sites of a region of an aquaculture industry. The study was a theoretical one examining the effect of transmission parameters through a sensitivity analysis. Production was modelled as either dispersed over many sites, or concentrated into small areas to provide 'firebreaks' between such areas as a disease control strategy. The effectiveness of such a control strategy could then be examined for different industry and disease parameters (for example, overall production, and rates of within-and between-site infection). At the within-site level, contact was modelled as either frequency or density dependent, either of these extreme formulations being potentially appropriate for different diseases. Under density dependence, the effect of high host density of increasing the basic reproduction number R 0 dominates, in contrast to the frequency dependent model. However, for both model types, concentration of production into separate areas successfully slows the spread of simulated disease, particularly where long-distance transmission of the pathogen is weak due to fast attenuation of infectious agent over distance and time.

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Section: Model Behaviour and Assumptionsmentioning

confidence: 99%

Section: Practical Application Of the Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A strategic model for epidemic control in aquaculture

Green

2010

Preventive Veterinary Medicine

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“…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.…”

mentioning

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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“…They have been used for modelling foot and mouth disease (FMD) ) and avian influenza (Dent et al 2008), amongst other diseases. These models are valuable because they can identify farms that are important in the spread of pathogens and provide a valuable tool for designing and investigating the effectiveness of control strategies .Contact between farms often shows a large variation in the number, timing and direction of contacts (Thrush & Peeler 2006, Green et al 2009. Heterogeneity, i.e.…”

mentioning

confidence: 99%

Seasonality and heterogeneity of live fish movements in Scottish fish farms

Werkman¹,

Green²,

Munro³

et al. 2011

Dis. Aquat. Org.

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Movement of live animals is a key contributor to disease spread. Farmed Atlantic salmon Salmo salar, rainbow trout Onchorynchus mykiss and brown/sea trout Salmo trutta are initially raised in freshwater (FW) farms; all the salmon and some of the trout are subsequently moved to seawater (SW) farms. Frequently, fish are moved between farms during their FW stage and sometimes during their SW stage. Seasonality and differences in contact patterns across production phases have been shown to influence the course of an epidemic in livestock; however, these parameters have not been included in previous network models studying disease transmission in salmonids. In Scotland, farmers are required to register fish movements onto and off their farms; these records were used in the present study to investigate seasonality and heterogeneity of movements for each production phase separately for farmed salmon, rainbow trout and brown/sea trout. Salmon FW-FW and FW-SW movements showed a higher degree of heterogeneity in number of contacts and different seasonal patterns compared with SW-SW movements. FW-FW movements peaked from May to July and FW-SW movements peaked from March to April and from October to November. Salmon SW-SW movements occurred more consistently over the year and showed fewer connections and number of repeated connections between farms. Therefore, the salmon SW-SW network might be treated as homogeneous regarding the number of connections between farms and without seasonality. However, seasonality and production phase should be included in simulation models concerning FW-FW and FW-SW movements specifically. The number of rainbow trout FW-FW and brown/sea trout FW-FW movements were different from random. However, movements from other production phases were too low to discern a seasonal pattern or differences in contact pattern. KEY WORDS: Disease transmission · Epidemiology · Contact structure · Aquaculture Resale or republication not permitted without written consent of the publisherDis Aquat Org 96: [69][70][71][72][73][74][75][76][77][78][79][80][81][82] 2011 (hatcheries) to the bottom (smolt producers or ongrowers), which can be compared with the movement structure of industries such as of pigs (Lindstrom et al. 2010) and poultry (Cox & Pavic 2010).Live fish movements are a risk for pathogen transmission between farms (Murray et al. 2002, Murray & Peeler 2005. Pathogens can also be introduced by other pathways such as well-boat visits (Murray et al. 2002) and on a local level by water movement (Jonkers et al. 2010) or by wild fish movements (Uglem et al. 2009). Disease outbreaks can cause reduced appetite, reduced growth and increased mortality rates, depending on the disease (OIE 2009), reducing production and profitability (Murray & Peeler 2005). In addition, disease outbreaks can cause welfare problems (Turnbull & Kadri 2007), and pathogen accumulation in fish farms may lead to transmission of pathogens to wild fish populations (Wallace et al. 2008).If fish are infected and transported there ...

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Small- and large-scale network structure of live fish movements in Scotland

Cited by 48 publications

References 31 publications

A strategic model for epidemic control in aquaculture

A strategic model for epidemic control in aquaculture

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

Seasonality and heterogeneity of live fish movements in Scottish fish farms

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