In areas with many mallards, mute swans and brent geese, the risk of an avian flu outbreak is higher. This data can help predict the risk of an avian flu outbreak on poultry farms. This is the conclusion of PhD research by Janneke Schreuder of Wageningen University & Research in collaboration with Utrecht University and Sovon Vogelonderzoek.
As part of her PhD research, Schreuder investigated the environmental factors of 26 outbreaks (the blue dots on the map) with highly pathogenic bird flu on Dutch poultry farms, with some farms having been affected multiple times by an outbreak. She compared these with factors surrounding 104 non-infected control farms (white points).
The risk of bird flu is highest in the red areas © Eigen Foto
The researcher used ‘machine learning’ to analyze predictive indicators for bird flu outbreaks. Wild bird densities proved to be the best predictive indicators. Nineteen of the twenty highest scoring avian flu risk indicators involved wild birds. Of these, seventeen were waterfowl and two birds of prey. In addition to wild birds, one of the landscape features, agricultural use, also contributed to the prediction of bird flu on poultry farms.
Mallard important
Of the waterfowl, the presence and numbers of mallard ducks and mute swans appeared to be the most important contributors to risk prediction, followed by brent goose and widgeon. ‘The highly pathogenic virus variants have been found in several recent bird flu outbreaks in many of these waterfowl, as have several other species on the list of best predictive bird species,’ the researcher reports. ‘The research shows that presence is a risk factor. How the contamination then enters the company requires further investigation.’
Landscape features such as open water do play a role in the presence of wild water birds. ‘My research shows that looking at the presence of wild waterfowl species and their numbers is a better indicator for predicting bird flu than looking at the presence of landscape features.’
Risk map
The research will lead to a model to predict the risk of bird flu based on the counts and species of wild birds. Schreuder has made a risk map with this model. The risk of bird flu is highest in the red areas on the map and lowest in the dark green areas.
‘Using that map you can give priority to certain areas in bird flu monitoring. And, for example, the targeted use of preventive measures to prevent the introduction of bird flu. In addition, the maps can help determine where poultry farms can best be located to minimize the risk of an avian flu outbreak.’
Predictive Factors
The research into predictive factors for bird flu outbreaks was part of the PhD research at the University of Utrecht. She worked together with Wageningen University & Research and Sovon Vogelonderzoek Nederland.
This research was part of the top sector project Fight Flu, financed by the Ministry of Agriculture, Nature and Food Quality and Avined. The scientific publication on research into predictive indicators for bird flu outbreaks on poultry farms was recently published in Pathogens.
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