Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach.

Journal: PLoS neglected tropical diseases
PMID: 39820517

Abstract

BACKGROUND: Leptospirosis is a neglected zoonotic disease prevalent worldwide, particularly in tropical regions experiencing frequent rainfall and severe cyclones, which are further aggravated by climate change. This bacterial zoonosis, caused by the Leptospira genus, can be transmitted through contaminated water and soil. The Pacific islands bear a high burden of leptospirosis, making it crucial to identify key factors influencing its distribution. Understanding these factors is vital for developing targeted policy decisions to mitigate the spread of Leptospira.

Authors

  • Rodrigue Govan
    Institute of Exact and Applied Sciences, University of New Caledonia, Nouméa, Province Sud, New Caledonia.
  • Romane Scherrer
    Institute of Exact and Applied Sciences, University of New Caledonia, Nouméa, Province Sud, New Caledonia.
  • Baptiste Fougeron
    Institute of Exact and Applied Sciences, University of New Caledonia, Nouméa, Province Sud, New Caledonia.
  • Christine Laporte-Magoni
    Sciences and Technology Department, University of New Caledonia, Nouméa, Province Sud, New Caledonia.
  • Roman Thibeaux
    Pasteur Institute of New Caledonia, Nouméa, New Caledonia.
  • Pierre Genthon
    HydroSciences Montpellier, University of Montpellier, CNRS, IRD, Nouméa, New Caledonia.
  • Philippe Fournier-Viger
    College of Computer Science and Software Engineering, Big Data Institute, Shenzhen University, Shenzhen, China.
  • Cyrille Goarant
    Pasteur Institute of New Caledonia, Nouméa, New Caledonia.
  • Nazha Selmaoui-Folcher
    Institute of Exact and Applied Sciences, University of New Caledonia, Nouméa, Province Sud, New Caledonia.

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