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

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    Francis M Mutuku et al.pdf (1.238Mb)
    Date
    2021
    Author
    Jamie M. Caldwell
    A. Desiree LaBeaud
    Eric F. Lambin
    Anna M. Stewart-Ibarra
    Bryson A. Ndenga
    Francis M. Mutuku
    Amy R. Krystosik
    Efraín Beltrán Ayala
    Assaf Anyamba
    Mercy J. Borbor-Cordova
    Richard Damoah
    Elysse N. Grossi-Soyster
    Froilán Heras Heras
    Harun N. Ngugi
    Sadie J. Ryan
    Melisa M. Shah
    Rachel Sippy
    Erin A. Mordecai
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    Abstract
    Climate 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.
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    https://ir.tum.ac.ke/handle/123456789/17450
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