Climate-driven models of leptospirosis dynamics in tropical islands from three oceanic basins.


Journal

PLoS neglected tropical diseases
ISSN: 1935-2735
Titre abrégé: PLoS Negl Trop Dis
Pays: United States
ID NLM: 101291488

Informations de publication

Date de publication:
25 Apr 2024
Historique:
received: 12 10 2023
accepted: 05 04 2024
medline: 25 4 2024
pubmed: 25 4 2024
entrez: 25 4 2024
Statut: aheadofprint

Résumé

Leptospirosis is a neglected zoonosis which remains poorly known despite its epidemic potential, especially in tropical islands where outdoor lifestyle, vulnerability to invasive reservoir species and hot and rainy climate constitute higher risks for infections. Burden remains poorly documented while outbreaks can easily overflow health systems of these isolated and poorly populated areas. Identification of generic patterns driving leptospirosis dynamics across tropical islands would help understand its epidemiology for better preparedness of communities. In this study, we aim to model leptospirosis seasonality and outbreaks in tropical islands based on precipitation and temperature indicators. We adjusted machine learning models on leptospirosis surveillance data from seven tropical islands (Guadeloupe, Reunion Island, Fiji, Futuna, New Caledonia, and Tahiti) to investigate 1) the effect of climate on the disease's seasonal dynamic, i.e., the centered seasonal profile and 2) inter-annual anomalies, i.e., the incidence deviations from the seasonal profile. The model was then used to estimate seasonal dynamics of leptospirosis in Vanuatu and Puerto Rico where disease incidence data were not available. A robust model, validated across different islands with leave-island-out cross-validation and based on current and 2-month lagged precipitation and current and 1-month lagged temperature, can be constructed to estimate the seasonal dynamic of leptospirosis. In opposition, climate determinants and their importance in estimating inter-annual anomalies highly differed across islands. Climate appears as a strong determinant of leptospirosis seasonality in tropical islands regardless of the diversity of the considered environments and the different lifestyles across the islands. However, predictive and expandable abilities from climate indicators weaken when estimating inter-annual outbreaks and emphasize the importance of these local characteristics in the occurrence of outbreaks.

Sections du résumé

BACKGROUND BACKGROUND
Leptospirosis is a neglected zoonosis which remains poorly known despite its epidemic potential, especially in tropical islands where outdoor lifestyle, vulnerability to invasive reservoir species and hot and rainy climate constitute higher risks for infections. Burden remains poorly documented while outbreaks can easily overflow health systems of these isolated and poorly populated areas. Identification of generic patterns driving leptospirosis dynamics across tropical islands would help understand its epidemiology for better preparedness of communities. In this study, we aim to model leptospirosis seasonality and outbreaks in tropical islands based on precipitation and temperature indicators.
METHODOLOGY/PRINCIPAL FINDINGS RESULTS
We adjusted machine learning models on leptospirosis surveillance data from seven tropical islands (Guadeloupe, Reunion Island, Fiji, Futuna, New Caledonia, and Tahiti) to investigate 1) the effect of climate on the disease's seasonal dynamic, i.e., the centered seasonal profile and 2) inter-annual anomalies, i.e., the incidence deviations from the seasonal profile. The model was then used to estimate seasonal dynamics of leptospirosis in Vanuatu and Puerto Rico where disease incidence data were not available. A robust model, validated across different islands with leave-island-out cross-validation and based on current and 2-month lagged precipitation and current and 1-month lagged temperature, can be constructed to estimate the seasonal dynamic of leptospirosis. In opposition, climate determinants and their importance in estimating inter-annual anomalies highly differed across islands.
CONCLUSIONS/SIGNIFICANCE CONCLUSIONS
Climate appears as a strong determinant of leptospirosis seasonality in tropical islands regardless of the diversity of the considered environments and the different lifestyles across the islands. However, predictive and expandable abilities from climate indicators weaken when estimating inter-annual outbreaks and emphasize the importance of these local characteristics in the occurrence of outbreaks.

Identifiants

pubmed: 38662800
doi: 10.1371/journal.pntd.0011717
pii: PNTD-D-23-01281
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0011717

Informations de copyright

Copyright: © 2024 Douchet et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Léa Douchet (L)

ENTROPIE, IRD, Univ Reunion, CNRS, IFREMER, Univ Nouvelle Calédonie, Nouméa, New Caledonia.
ESPACE-DEV, IRD, Univ Montpellier, Univ. Antilles, Univ Guyane, Univ Réunion, Phnom Penh, Cambodia.

Christophe Menkes (C)

ENTROPIE, IRD, Univ Reunion, CNRS, IFREMER, Univ Nouvelle Calédonie, Nouméa, New Caledonia.

Vincent Herbreteau (V)

ESPACE-DEV, IRD, Univ Montpellier, Univ. Antilles, Univ Guyane, Univ Réunion, Phnom Penh, Cambodia.
Institut Pasteur du Cambodge, Epidemiology and Public Health Unit, Phnom Penh, Cambodia.

Joséphine Larrieu (J)

ENTROPIE, IRD, Univ Reunion, CNRS, IFREMER, Univ Nouvelle Calédonie, Nouméa, New Caledonia.

Margot Bador (M)

CECI Université de Toulouse, CERFACS/CNRS, Toulouse, France.

Cyrille Goarant (C)

Institut Pasteur in New Caledonia, Leptospirosis Research and Expertise Unit, Nouméa, New Caledonia.
Public Health Division, The Pacific Community, Nouméa, New Caledonia.

Morgan Mangeas (M)

ENTROPIE, IRD, Univ Reunion, CNRS, IFREMER, Univ Nouvelle Calédonie, Nouméa, New Caledonia.

Classifications MeSH