Many studies have estimated empirical relationships between dengue, weather, and El Ni˜no
in several regions of the world. Some of these studies used their model estimations to predict
the potential impacts of climate change on the future distribution of dengue. Often,
these studies have sidestepped elements that are key to the estimation of the effects of climate
variables on dengue with statistical confidence. For example, they fail to incorporate
covariates that may confound the empirical associations between dengue, weather, El Ni˜no,
and climate undermining their model estimations. Additionally, several studies used nationally
or supra-nationally aggregated data which remove the spatial variability in all variables
making it difficult to detect complex associations between dengue and climate variables.
Other studies were conducted in small geographical areas with the problem of having low
numbers of disease cases posing problems for their analysis with statistical confidence.
Here, we used the most comprehensive dengue-related datasets analysed to date and
several statistical methods to investigate the effects of weather, climate, and El Ni˜no on
dengue incidence. We demonstrate that such effects are robust to the confounding effects
of socioeconomic development and other non-climatic factors such as seasonal trends and
inter-annual variability. Our results reveal that the effects weather and El Ni˜no are significantly
heterogeneous between provinces influenced by the underlying climate. With the
exception of access to piped water, we could not identify significant effects of socioeconomic
status on dengue occurrence. This result is likely related to human behaviour or the
lack of protective measures against mosquitoes. We used our model estimations to project
the potential impacts of climate change on dengue incidence by 2030, 2050 and 2080 with
greater statistical confidence than previous studies. Our projections indicate that climate
change is likely to increase dengue incidence mainly in already endemic areas
