RT Journal Article
SR Electronic
T1 Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.07.938720
DO 10.1101/2020.02.07.938720
A1 Jamie M. Caldwell
A1 A. Desiree LaBeaud
A1 Eric F. Lambin
A1 Anna M. Stewart-Ibarra
A1 Bryson A. Ndenga
A1 Francis M. Mutuku
A1 Amy R. Krystosik
A1 Efraín Beltrán Ayala
A1 Assaf Anyamba
A1 Mercy J. Borbor-Cordova
A1 Richard Damoah
A1 Elysse N. Grossi-Soyster
A1 Froilán Heras Heras
A1 Harun N. Ngugi
A1 Sadie J. Ryan
A1 Melisa M. Shah
A1 Rachel Sippy
A1 Erin A. Mordecai
YR 2021
UL http://biorxiv.org/content/early/2021/01/13/2020.02.07.938720.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.