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, Erin A Mordecai

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

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.

Original languageEnglish
Article number1233
Number of pages13
JournalNature Communications
Volume12
DOIs
Publication statusPublished - 23 Feb 2021

Keywords

  • Animals
  • Basic reproduction number
  • Climate change
  • Culicidae/physiology
  • Disease outbreaks
  • Ecuador/epidemiology
  • Geography
  • Humans
  • Kenya/epidemiology
  • Models, biological
  • Nonlinear dynamics
  • Socioeconomic factors
  • Spatio-temporal analysis
  • Time factors
  • Vector borne diseases/epidemiology

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