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

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 and Erin A. Mordecai
Additional contact information
Jamie M. Caldwell: Stanford University
A. Desiree LaBeaud: Stanford University
Eric F. Lambin: Stanford University
Anna M. Stewart-Ibarra: SUNY Upstate Medical University
Bryson A. Ndenga: Centre for Global Health Research, Kenya Medical Research Institute
Francis M. Mutuku: Technical university of Mombasa
Amy R. Krystosik: Stanford University
Efraín Beltrán Ayala: Technical University of Machala
Assaf Anyamba: Universities Space Research Association and NASA Goddard Space Flight Center
Mercy J. Borbor-Cordova: Facultad de Ingeniería Marítima y Ciencias del Mar, Escuela Superior Politécnica del Litoral, ESPOL
Richard Damoah: Morgan State University and NASA Goddard Space Flight Center
Elysse N. Grossi-Soyster: Stanford University
Froilán Heras Heras: Center for Research SUNY-Upstate-Teófilo Dávila Hospital
Harun N. Ngugi: Chuka University
Sadie J. Ryan: University of Florida
Melisa M. Shah: Stanford University
Rachel Sippy: Center for Research SUNY-Upstate-Teófilo Dávila Hospital
Erin A. Mordecai: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-21496-7

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