Human Contacts and Potential Pathways of Disease Introduction on Georgia Poultry Farms

College of Veterinary Medicine, Department of Large Animal Medicine, University of Georgia, Athens, GA 30602, USA.
Avian Diseases (Impact Factor: 1.24). 04/2009; 53(1):55-62. DOI: 10.1637/8593.1
Source: PubMed


As highly pathogenic avian influenza H5N1 virus continues to circulate in the world, poultry farm biosecurity and timely reporting of morbidity and mortality among commercial poultry farms in the United States are major concerns. To assess the vulnerability of poultry farms to the introduction and spread of a highly infectious pathogen, such as the currently circulating H5N1 influenza virus, a survey was administered to growers in two counties in Georgia representing areas of low and high poultry densities. Survey questions regarding horizontal contacts and management were sent to commercial broiler and breeder-layer chicken producers. Responses were used to estimate and compare contact rates and patterns between the two regions. The distribution of high-risk visitors (i.e., those going inside the poultry houses) to poultry farms did not vary significantly between growers in counties with high and low poultry densities or between breeder-layer and broiler growers. Compared with broiler producers in the county with high poultry density, broiler growers in the county with low poultry density were more likely to hire non-family employees to help with poultry management (62% vs. 17%; P = 0.001) and assist other growers with their poultry (31% vs. 6%; P = 0.025). Use of contracted litter services was significantly higher (P = 0.019) among broiler growers in the poultry-dense county (40%) compared with the low-density county (6%). Compared with broiler growers, breeder-layer producers also were significantly more likely to hire non-family employees to help on the farm (53% vs. 17%; P = 0.008). Poultry growers in the highly poultry-dense county were more likely to have a public road or field receiving poultry litter within a quarter mile of their poultry houses, compared with those in the lower density county. Data obtained in this study support the observations of published poultry disease outbreak investigations and highlight the differences in farm vulnerability to disease introduction within areas of different poultry densities and management practices.

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    • "However, MTs are recognised to vary according to several factors such as production type, management practices and bird age and a wider likely range of 0.03–3.33% was found for poultry producers in Georgia, USA (Vieira et al., 2009). As the likely MT triggering HPAI detection in British poultry farms is not known we considered a range of MTs and present results for an intermediate threshold of 0.5%, corresponding to the Dutch recommendation. "
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    ABSTRACT: The importance of considering coupled interactions across multiple population scales has not previously been studied for highly pathogenic avian influenza (HPAI) in the British commercial poultry industry. By simulating the within-flock transmission of HPAI using a deterministic S-E-I-R model, and by incorporating an additional environmental class representing infectious faeces, we tracked the build-up of infectious faeces within a poultry house over time. A measure of the transmission risk (TR) was computed for each farm by linking the amount of infectious faeces present each day of an outbreak with data describing the daily on-farm visit schedules for a major British catching company. Larger flocks tended to have greater levels of these catching-team visits. However, where density-dependent contact was assumed, faster outbreak detection (according to an assumed mortality threshold) led to a decreased opportunity for catching-team visits to coincide with an outbreak. For this reason, maximum TR-levels were found for mid-range flock sizes (~25,000-35,000 birds). When assessing all factors simultaneously using multivariable linear regression on the simulated outputs, those related to the pattern of catching-team visits had the largest effect on TR, with the most important movement-related factor depending on the mode of transmission. Using social network analysis on a further database to inform a measure of between-farm connectivity, we identified a large fraction of farms (28%) that had both a high TR and a high potential impact at the between farm level. Our results have counter-intuitive implications for between-farm spread that could not be predicted based on flock size alone, and together with further knowledge of the relative importance of transmission risk and impact, could have implications for improved targeting of control measures.
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    • "* Reference population is a farm that is not affiliated with the integrator group of the index farm at the same model parameters ( viral survival and duration of infectiousness ) . Confidence intervals obtained using nonparametric bootstrapping resampling in Crystal Ball . doi : 10 . 1371 / journal . pone . 0009888 . t004 with Vieira et al . ( 2009 ) , who conducted a similar survey of poultry growers in Georgia , US [ 48 ] . As any stochastic model consists of a simplified representation of reality , we made a number of key assumptions in our analysis . For example , we did not include environmental sources of transmis - sion ( wild animal movement , or wind or water transport , "
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    ABSTRACT: Models of between-farm transmission of pathogens have identified service vehicles and social groups as risk factors mediating the spread of infection. Because of high levels of economic organization in much of the poultry industry, we examined the importance of company affiliation, as distinct from social contacts, in a model of the potential spread of avian influenza among broiler poultry farms in a poultry-dense region in the United States. The contribution of company affiliation to risk of between-farm disease transmission has not been previously studied.
    Full-text · Article · Mar 2010 · PLoS ONE
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