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 language | English |
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Article number | 1233 |
Number of pages | 13 |
Journal | Nature Communications |
Volume | 12 |
DOIs | |
Publication status | Published - 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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Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents (dataset)
Sippy, R. (Creator), GitHub, 2021
https://github.com/jms5151/SEI-SEIR_Arboviruses
Dataset