A support vector machine based approach for predicting the risk of freshwater disease emergence in England

Hossein Hassani*, Emmanuel S. Silva, Marine Combe, Demetra Andreou, Mansi Ghodsi, Mohammad Reza Yeganegi, Rodolphe E. Gozlan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
32 Downloads (Pure)

Abstract

Disease emergence, in the last decades, has had increasingly disproportionate impacts on aquatic freshwater biodiversity. Here, we developed a new model based on Support Vector Machines (SVM) for predicting the risk of freshwater fish disease emergence in England. Following a rigorous training process and simulations, the proposed SVM model was validated and reported high accuracy rates for predicting the risk of freshwater fish disease emergence in England. Our findings suggest that the disease monitoring strategy employed in England could be successful at preventing disease emergence in certain parts of England, as areas in which there were high fish introductions were not correlated with high disease emergence (which was to be expected from the literature). We further tested our model’s predictions with actual disease emergence data using Chi-Square tests and test of Mutual Information. The results identified areas that require further attention and resource allocation to curb future freshwater disease emergence successfully.
Original languageEnglish
Pages (from-to)89-103
Number of pages15
JournalStats
Volume2
Issue number1
Early online date5 Feb 2019
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes

Keywords

  • biodiversity
  • conservation
  • management
  • policies
  • non native introduction
  • forecasting
  • support vector machines

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