Abstract
BackgroundTargeted sampling can capture the characteristics of more vulnerable sectors of a population, but may bias the picture of population level disease risk. When sampling network data, an incomplete description of the population may arise leading to biased estimates of between-host connectivity. Avian influenza (AI) control planning in Great Britain (GB) provides one example where network data for the poultry industry (the Poultry Network Database or PND), targeted large premises and is consequently demographically biased. Exposing the effect of such biases on the geographical distribution of network properties could help target future poultry network data collection exercises. These data will be important for informing the control of potential future disease outbreaks.ResultsThe PND was used to compute between-farm association frequencies, assuming that farms sharing the same slaughterhouse or catching company, or through integration, are potentially epidemiologically linked. The fitted statistical models were extrapolated to the Great Britain Poultry Register (GBPR); this dataset is more representative of the poultry industry but lacks network information. This comparison showed how systematic biases in the demographic characterisation of a network, resulting from targeted sampling procedures, can bias the derived picture of between-host connectivity within the network.ConclusionsWith particular reference to the predictive modeling of AI in GB, we find significantly different connectivity patterns across GB when network estimates incorporate the more demographically representative information provided by the GBPR; this has not been accounted for by previous epidemiological analyses. We recommend ranking geographical regions, based on relative confidence in extrapolated estimates, for prioritising further data collection. Evaluating whether and how the between-farm association frequencies impact on the risk of between-farm transmission will be the focus of future work.
Highlights
Targeted sampling can capture the characteristics of more vulnerable sectors of a population, but may bias the picture of population level disease risk
The poultry industry network The Poultry Network Database (PND), with between-farm associations assumed to arise through shared industry contacts, was highly connected: most farms were potentially associated with almost all other farms, mostly through slaughterhouses (SHs) and catching companies (CCs) (Figures 1)
Scenario 1: predictors of large between-farm association frequency Equation 1 shows the form of the logistic model used to identify predictors of a large between-farm association frequency (Laf; referred to as scenario 1, see Methods for further details)
Summary
Targeted sampling can capture the characteristics of more vulnerable sectors of a population, but may bias the picture of population level disease risk. Avian influenza (AI) control planning in Great Britain (GB) provides one example where network data for the poultry industry (the Poultry Network Database or PND), targeted large premises and is demographically biased. Exposing the effect of such biases on the geographical distribution of network properties could help target future poultry network data collection exercises These data will be important for informing the control of potential future disease outbreaks. In the absence of epidemic data, and without the ability to validate predictive models for AI control in GB, mathematical models are a valuable tool for exploring the connectivity of the poultry industry These epidemiological models have investigated the efficacy of current control measures for AI in GB and have identified particular scenarios that could result in a large outbreak [14,15,16]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
