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Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

المناخ يتنبأ بالتباين الجغرافي والزماني في ديناميكيات الأمراض التي ينقلها البعوض في قارتين

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Journal , Preprint 07 Feb 2020 United Kingdom Publisher:Springer Science and Business Media LLCJournal:Nature Communications, volume 12 (eissn: 2041-1723,

Copyright policy )Funded by:NIH | Leveraging environmental ..., NSF | How land use change trans..., NSF | Effects of temperature on... +2 projects

Authors: Melisa M Shah; Froilán Heras; Bryson A. Ndenga; Eric F. Lambin; Eric F. Lambin; A. Desiree LaBeaud; Jamie M. Caldwell; +15 Authors

doi: 10.1038/s41467-021-21496-7 , 10.1101/2020.02.07.938720 , 10.60692/knn8a-3z168 , 10.60692/63ha6-q6s39

pmid: 33623008

pmc: PMC7902664

handle: 10023/24716

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

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Abstract

AbstractClimate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

Country

United Kingdom

Related Organizations

Florida Southern College
United States
Stanford University
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University of Florida
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Kenya Medical Research Institute
Kenya
Goddard Space Flight Center
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Keywords

Time Factors, Basic Reproduction Number, FOS: Health sciences, Vector borne diseases/epidemiology, Biochemistry, Gene, Disease Outbreaks, Context (archaeology), Aedes aegypti, Models, RA0421, Aedes, RA0421 Public health. Hygiene. Preventive Medicine, SDG 13 - Climate Action, Climate change, Disease outbreaks, Vector (molecular biology), Climatology, GE, Ecology, Geography, Q, Geology, QR Microbiology, Basic reproduction number, Infectious Diseases, Environmental health, Archaeology, Larva, Medicine, Ecuador, Viral Hemorrhagic Fevers and Zoonotic Infections, GE Environmental Sciences, Science, Climate Change, Population, Culicidae/physiology, Vector Borne Diseases, Kenya/epidemiology, Mosquito Vectors, Socioeconomic factors, Spatio-temporal analysis, Models, Biological, Climate model, 333, Spatio-Temporal Analysis, SDG 3 - Good Health and Well-being, Ecuador/epidemiology, Virology, Health Sciences, Animals, Humans, Global Impact of Arboviral Diseases, Biology, Recombinant DNA, Time factors, Public Health, Environmental and Occupational Health, DAS, Outbreak, FOS: Earth and related environmental sciences, Dengue fever, Kenya, QR, Malaria, Culicidae, Nonlinear Dynamics, Socioeconomic Factors, Nonlinear dynamics, FOS: Biological sciences, Chikungunya, biological

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