AI Article Synopsis

  • HPAIV transmission from wild birds to poultry poses significant risks to animal and public health globally.
  • A case-control study on Dutch poultry farms identified key predictors for HPAI outbreak risk, heavily linked to the densities of specific water-associated bird species.
  • Machine learning models effectively distinguished outbreak cases from controls, producing reliable risk maps to guide surveillance and biosecurity efforts.

Article Abstract

Highly pathogenic avian influenza viruses' (HPAIVs) transmission from wild birds to poultry occurs globally, threatening animal and public health. To predict the HPAI outbreak risk in relation to wild bird densities and land cover variables, we performed a case-control study of 26 HPAI outbreaks (cases) on Dutch poultry farms, each matched with four comparable controls. We trained machine learning classifiers to predict outbreak risk with predictors analyzed at different spatial scales. Of the 20 best explaining predictors, 17 consisted of densities of water-associated bird species, 2 of birds of prey, and 1 represented the surrounding landscape, i.e., agricultural cover. The spatial distribution of mallard () contributed most to risk prediction, followed by mute swan (), common kestrel () and brant goose (). The model successfully distinguished cases from controls, with an area under the receiver operating characteristic curve of 0.92, indicating accurate prediction of HPAI outbreak risk despite the limited numbers of cases. Different classification algorithms led to similar predictions, demonstrating robustness of the risk maps. These analyses and risk maps facilitate insights into the role of wild bird species and support prioritization of areas for surveillance, biosecurity measures and establishments of new poultry farms to reduce HPAI outbreak risks.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143584PMC
http://dx.doi.org/10.3390/pathogens11050549DOI Listing

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