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Background: There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors. Methods and Findings: Using a Generalized Additive Model, we estimated statistically significant effects of weather and access to piped water on dengue. The effects of weather were highly nonlinear. Minimum temperature (Tmin) had almost no effect on dengue incidence below 5°C, but Tmin values above 18°C showed a rapidly increasing effect. Maximum temperature above 20°C also showed an increasing effect on dengue incidence with a peak around 32°C, after which the effect declined. There is also an increasing effect of precipitation as it rose to about 550 mm, beyond which such effect declines. Rising access to piped water was related to increasing dengue incidence. We used our model estimations to project the potential impact of climate change on dengue incidence under three emission scenarios by 2030, 2050, and 2080. An increase of up to 40% in dengue incidence by 2080 was estimated under climate change while holding the other driving factors constant. Conclusions: Our results indicate that weather significantly influences dengue incidence in Mexico and that such relationships are highly nonlinear. These findings highlight the importance of using flexible model specifications when analysing weather–health interactions. Climate change may contribute to an increase in dengue incidence. Rising access to piped water may aggravate dengue incidence if it leads to increased domestic water storage. Climate change may therefore influence the success or failure of future efforts against dengue. Author Summary: Relationships between weather and mosquito-borne diseases are nonlinear in nature. This means that the number of disease cases does not vary equally with changes in the climate system. Identifying adequately the form of the relationship between disease outcomes and their drivers in an empirical fashion can be tedious and imprecise. Here, we use a statistical modelling approach that estimates the form of the relationships between dengue and weather in an automated way. We use this approach to analyse a comprehensive dataset covering 23 years of dengue reports from Mexico. Our model incorporates the effects of some non-climatic factors that are key for disease occurrence. We then use our estimations to project the potential impact of climate change on dengue incidence under three different scenarios for three different time periods. The estimated effects of weather on dengue were highly nonlinear. These results highlight the importance of using flexible modelling approaches for the analysis of disease-weather relationships with a nonlinear behaviour. Rising access to water supply was related to increases in dengue incidence. This situation may be related to increased water storage induced by unreliable water supply. Dengue incidence may increase to about 40% by 2080 due to climate change. This increase in dengue incidence may be aggravated by a rising access to piped water if it leads to domestic water storage, although any adaptation measures to rising dengue may also affect the risk. Our results contribute to a better overall understanding of the epidemiology of dengue. [ABSTRACT FROM AUTHOR]

Multiple linear regression models were fitted to look for associations between changes in the incidence rate of dengue fever and climate variability in the warm and humid region of Mexico. Data were collected for 12 Mexican provinces over a 23-year period (January 1985 to December 2007). Our results show that the incidence rate or risk of infection is higher during El Niño events and in the warm and wet season. We provide evidence to show that dengue fever incidence was positively associated with the strength of El Niño and the minimum temperature, especially during the cool and dry season. Our study complements the understanding of dengue fever dynamics in the region and may be useful for the development of early warning systems.