Indian Ocean temperature anomalies predict long-term global dengue trends.


Journal

Science (New York, N.Y.)
ISSN: 1095-9203
Titre abrégé: Science
Pays: United States
ID NLM: 0404511

Informations de publication

Date de publication:
10 May 2024
Historique:
medline: 10 5 2024
pubmed: 10 5 2024
entrez: 9 5 2024
Statut: ppublish

Résumé

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Identifiants

pubmed: 38723095
doi: 10.1126/science.adj4427
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

639-646

Auteurs

Yuyang Chen (Y)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.
Yangtze Eco-Environment Engineering Research Center, China Three Gorges Corporation, Wuhan, China.

Yiting Xu (Y)

School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, China.

Lin Wang (L)

Department of Genetics, University of Cambridge, Cambridge, UK.

Yilin Liang (Y)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

Naizhe Li (N)

School of National Safety and Emergency Management, Beijing Normal University, Zhuhai, China.

José Lourenço (J)

Católica Biomedical Research Center, Católica Medical School, Universidade Católica Portuguesa, Lisbon, Portugal.

Yun Yang (Y)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

Qiushi Lin (Q)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

Ligui Wang (L)

Center of Disease Control and Prevention, PLA, Beijing, China.

He Zhao (H)

CMA Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, China.

Bernard Cazelles (B)

Institut de Biologie de l'École Normale Supérieure UMR 8197, Eco-Evolutionary Mathematics, École Normale Supérieure, Paris, France.
Unité Mixte Internationnale 209, Mathematical and Computational Modeling of Complex Systems, Sorbonne Université, Paris, France.

Hongbin Song (H)

Center of Disease Control and Prevention, PLA, Beijing, China.

Ziyan Liu (Z)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

Zengmiao Wang (Z)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

Oliver J Brady (OJ)

Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK.
Department of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.

Simon Cauchemez (S)

Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000, CNRS, Paris, France.

Huaiyu Tian (H)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Beijing Normal University, Beijing, China.

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Classifications MeSH