Risk for Low Pathogenicity Avian Influenza Virus on Poultry Farms, the Netherlands, 2007–2013

Bouwstra, Ruth; Gonzáles, José L.; Wit, Sjaak de; Stahl, Julia; Fouchier, Ron A. M.; Elbers, A.R.W.

doi:10.3201/eid2309.170276

Cited by 44 publications

(63 citation statements)

References 31 publications

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“…Broiler farm density has been looked at as potential predictor for HPAI occurrence, particularly in Asia [ 42 ], where farming conditions are different from those in California. In Europe and Canada, where farming systems are more similar to California, broiler farms are known to be at a lower risk than other types of poultry farming [ 43 ]. The fact that broiler farms density plays an important role in the models could be explained by two different scenarios: (i) migratory waterfowl potentially carrying AIV (LP or HP) are attracted to areas dense with broiler farms possibly due to the presence of feed and/or domestic fowls raised in open spaces; AIV could then be introduced into the farms via direct/indirect contact and lead to AI outbreaks; (ii) oversampling in areas that are highly dense for poultry farms (i.e.…”

Section: Discussionmentioning

confidence: 99%

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

et al. 2018

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The coexistence of different types of poultry operations such as free range and backyard flocks, large commercial indoor farms and live bird markets, as well as the presence of many areas where wild and domestic birds co-exist, make California susceptible to avian influenza outbreaks. The 2014–2015 highly pathogenic Avian Influenza (HPAI) outbreaks affecting California and other states in the United States have underscored the need for solutions to protect the US poultry industry against this devastating disease. We applied disease distribution models to predict where Avian influenza is likely to occur and the risk for HPAI outbreaks is highest. We used observations on the presence of Low Pathogenic Avian influenza virus (LPAI) in waterfowl or water samples at 355 locations throughout the state and environmental variables relevant to the disease epidemiology. We used two algorithms, Random Forest and MaxEnt, and two data-sets Presence-Background and Presence-Absence data. The models performed well (AUCc > 0.7 for testing data), particularly those using Presence-Background data (AUCc > 0.85). Spatial predictions were similar between algorithms, but there were large differences between the predictions with Presence-Absence and Presence-Background data. Overall, predictors that contributed most to the models included land cover, distance to coast, and broiler farm density. Models successfully identified several counties as high-to-intermediate risk out of the 8 counties with observed outbreaks during the 2014–2015 HPAI epizootics. This study provides further insights into the spatial epidemiology of AI in California, and the high spatial resolution maps may be useful to guide risk-based surveillance and outreach efforts.

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Section: Discussionmentioning

confidence: 99%

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

et al. 2018

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“…Based on the results, we performed a selection including all papers with explicit reference to risk factors associated with occurrence of LPAIV. A total of four papers (Gonzales, Stegeman, Koch, Wit, & Elbers, 2012; Tombari et al., ; Bouwstra et al., ; Belkhiria, Hijmans, Boyce, Crossley, & Martínez‐López, ) were selected, as they provided statistical support to the selection of risk factors according to the above‐mentioned criteria.…”

Section: Methodsmentioning

confidence: 99%

Spatial modelling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry

Sala

Burgos

Blanco³

et al. 2019

Transbound Emerg Dis

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Low pathogenicity avian influenza virus (LPAIV) is endemic in wild birds and poultry in Argentina, and active surveillance has been in place to prevent any eventual virus mutation into a highly pathogenic avian influenza virus (HPAIV), which is exotic in this country. Risk mapping can contribute effectively to disease surveillance and control systems, but it has proven a very challenging task in the absence of disease data. We used a combination of expert opinion elicitation, multicriteria decision analysis (MCDA) and ecological niche modelling (ENM) to identify the most suitable areas for the occurrence of LPAIV at the interface between backyard domestic poultry and wild birds in Argentina. This was achieved by calculating a spatially explicit risk index. As evidenced by the validation and sensitivity analyses, our model was successful in identifying high-risk areas for LPAIV occurrence. Also, we show that the risk for virus occurrence is significantly higher in areas closer to commercial poultry farms. Although the active surveillance systems have been successful in detecting LPAIV-positive backyard farms and wild birds in Argentina, our predictions suggest that surveillance efforts in those compartments could be improved by including high-risk areas identified by our model. Our research provides a tool to guide surveillance activities in the future, and presents a mixed methodological approach which could be implemented in areas where the disease is exotic or rare and a knowledge-driven modelling method is necessary.

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“…Small animals such as insects and worms may stimulate hunting and digging of chickens (Knierim, 2006). At the same time, free-range layer farms have-compared to indoor layer farms-a higher risk of introduction of low (LPAIV) and highly pathogenic avian influenza viruses (HPAIV) (Bouwstra et al, 2017;Gonzales, Stegeman, Koch, Wit, & Elbers, 2013;Kirunda et al, 2015;Terregino et al, 2007;Welby, Berg, et al, 2010a). The sheer majority of LPAIV introductions on poultry farms in the Netherlands takes place on free-range layer farms (Bouwstra et al, 2017;Gonzales et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, free-range layer farms have-compared to indoor layer farms-a higher risk of introduction of low (LPAIV) and highly pathogenic avian influenza viruses (HPAIV) (Bouwstra et al, 2017;Gonzales, Stegeman, Koch, Wit, & Elbers, 2013;Kirunda et al, 2015;Terregino et al, 2007;Welby, Berg, et al, 2010a). The sheer majority of LPAIV introductions on poultry farms in the Netherlands takes place on free-range layer farms (Bouwstra et al, 2017;Gonzales et al, 2013). Avian influenza viruses (AIV) are categorized as LPAIV or HPAIV, based on the pathobiological effects of the virus in chickens: in general LPAIV infections may be asymptomatic and produce no or mild disease in chickens (Gonzales & Elbers, 2018), while HPAIV infections produce high morbidity and mortality in poultry (Pantin-Jackwood & Swayne, 2009).…”

Section: Introductionmentioning

confidence: 99%

Quantification of visits of wild fauna to a commercial free‐range layer farm in the Netherlands located in an avian influenza hot‐spot area assessed by video‐camera monitoring

Elbers

Gonzáles

2019

Transbound Emerg Dis

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Free‐range poultry farms have a high risk of introduction of avian influenza viruses (AIV), and it is presumed that wild (water) birds are the source of introduction. There is very scarce quantitative data on wild fauna visiting free‐range poultry farms. We quantified visits of wild fauna to a free‐range area of a layer farm, situated in an AIV hot‐spot area, assessed by video‐camera monitoring. A total of 5,016 hr (209 days) of video recordings, covering all 12 months of a year, were analysed. A total of 16 families of wild birds and five families of mammals visited the free‐range area of the layer farm. Wild birds, except for the dabbling ducks, visited the free‐range area almost exclusively in the period between sunrise and the moment the chickens entered the free‐range area. Known carriers of AIV visited the outdoor facility regularly: species of gulls almost daily in the period January–August; dabbling ducks only in the night in the period November–May, with a distinct peak in the period December–February. Only a small fraction of visits of wild fauna had overlap with the presence of chickens at the same time in the free‐range area. No direct contact between chickens and wild birds was observed. It is hypothesized that AIV transmission to poultry on free‐range poultry farms will predominantly take place via indirect contact: taking up AIV by chickens via wild‐bird‐faeces‐contaminated water or soil in the free‐range area. The free‐range poultry farmer has several possibilities to potentially lower the attractiveness of the free‐range area for wild (bird) fauna: daily inspection of the free‐range area and removal of carcasses and eggs; prevention of forming of water pools in the free‐range facility. Furthermore, there are ways to scare‐off wild birds, for example use of laser equipment or trained dogs.

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Risk for Low Pathogenicity Avian Influenza Virus on Poultry Farms, the Netherlands, 2007–2013

Cited by 44 publications

References 31 publications

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

Spatial modelling for low pathogenicity avian influenza virus at the interface of wild birds and backyard poultry

Quantification of visits of wild fauna to a commercial free‐range layer farm in the Netherlands located in an avian influenza hot‐spot area assessed by video‐camera monitoring

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