Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic.

Dent, Jennifer E.; Kao, Rowland R.; Kiss, István Z.; Hyder, Kieran; Arnold, Mark

doi:10.1186/1746-6148-4-27

Cited by 73 publications

(76 citation statements)

References 28 publications

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“…Parameter estimation, an essential part of model development, is frequently based on data considered to be comparable to the system under study, while clearly being different. For example, because of the paucity of data specific for H5N1 transmission in the UK, models have relied upon extrapolation from other infectious agents, including those as different as bacteria [25]. Further, these models attempt to provide detailed representations of the potential transmission contacts between explicitly located populations of poultry (e.g.…”

Section: Uncertainties Of Anticipationmentioning

confidence: 99%

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Fish

Austin

Christley

et al. 2011

Phil. Trans. R. Soc. B

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Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy.

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Section: Uncertainties Of Anticipationmentioning

confidence: 99%

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Fish

Austin

Christley

et al. 2011

Phil. Trans. R. Soc. B

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show abstract

“…animals or farms. 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 .…”

Section: Introductionmentioning

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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“…Furthermore, targeted inoculation was here applied not as a replacement for, but as an addition to, the BC or EU policy, as detection and eradication are deemed indispensable for effective outbreak containment (Lee & Suarez 2005). Following existing advice (Albert et al 2000;Song et al 2005;Jeger et al 2007;Dent et al 2008), we focused first on hubs; all other sites we call peripherals. We define hubs here as having at least 40 links to other sites, thereby selecting the top 10.2 per cent of sites when ordered by connectivity.…”

Section: Targeted Inoculation Of Specific Sitesmentioning

confidence: 99%

Preventable H5N1 avian influenza epidemics in the British poultry industry network exhibit characteristic scales

Jonkers

Sharkey

Christley

2009

J. R. Soc. Interface.

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Epidemics are frequently simulated on redundantly wired contact networks, which have many more links between sites than are minimally required to connect all. Consequently, the modelled pathogen can travel numerous alternative routes, complicating effective containment strategies. These networks have moreover been found to exhibit 'scale-free' properties and percolation, suggesting resilience to damage. However, realistic H5N1 avian influenza transmission probabilities and containment strategies, here modelled on the British poultry industry network, show that infection dynamics can additionally express characteristic scales. These system-preferred scales constitute small areas within an observed power law distribution that exhibit a lesser slope than the power law itself, indicating a slightly increased relative likelihood. These characteristic scales are here produced by a networkpervading intranet of so-called hotspot sites that propagate large epidemics below the percolation threshold. This intranet is, however, extremely vulnerable; targeted inoculation of a mere 3 -6% (depending on incorporated biosecurity measures) of the British poultry industry network prevents large and moderate H5N1 outbreaks completely, offering an order of magnitude improvement over previously advocated strategies affecting the most highly connected 'hub' sites. In other words, hotspots and hubs are separate functional entities that do not necessarily coincide, and hotspots can make more effective inoculation targets. Given the ubiquity and relevance of networks (epidemics, Internet, power grids, protein interaction), recognition of this spreading regime elsewhere would suggest a similar disproportionate sensitivity to such surgical interventions.

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Contact structures in the poultry industry in Great Britain: Exploring transmission routes for a potential avian influenza virus epidemic.

Cited by 73 publications

References 28 publications

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Seasonality and heterogeneity of live fish movements in Scottish fish farms

Preventable H5N1 avian influenza epidemics in the British poultry industry network exhibit characteristic scales

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