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Predicting diarrhoea outbreaks with climate change.

Tassallah AbdullahiGeoff NitschkeNeville Sweijd
Published in: PloS one (2022)
Overall, experiments indicated that the prediction capacity of our DL methods (Convolutional Neural Networks) was found to be superior (with statistical significance) in terms of prediction accuracy across most provinces. This study's results have important implications for the development of automated early warning systems for diarrhoea (and related disease) outbreaks across the globe.
Keyphrases
  • convolutional neural network
  • climate change
  • deep learning
  • irritable bowel syndrome
  • machine learning
  • high throughput
  • risk assessment
  • drug induced