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Spatial prediction and validation of zoonotic hazard through micro-habitat properties: where does Puumala hantavirus hole - up?

Hussein KhalilGert OlssonMagnus MagnussonMagnus EvanderBirger HörnfeldtFrauke Ecke
Published in: BMC infectious diseases (2017)
Moist and mesic old spruce forests, with abundant cover such as large holes and bilberry shrubs, also providing food, were most likely to harbor infected bank voles. The models developed using long-term and spatially extensive data can be extrapolated to other areas in northern Fennoscandia. To predict the hazard of directly transmitted zoonoses in areas with unknown risk status, models based on micro-habitat variables and developed through machine learning techniques in well-studied systems, could be used.
Keyphrases
  • climate change
  • machine learning
  • human health
  • big data
  • artificial intelligence
  • deep learning