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We report a statistical mixed model for assessing the importance of climate and non-climate drivers of interannual variability in dengue fever in southern coastal Ecuador. Local climate data and Pacific sea surface temperatures (Oceanic Niño Index [ONI]) were used to predict dengue standardized morbidity ratios (SMRs; 1995–2010). Unobserved confounding factors were accounted for using non-structured yearly random effects. We found that ONI, rainfall, and minimum temperature were positively associated with dengue, with more cases of dengue during El Niño events. We assessed the influence of non-climatic factors on dengue SMR using a subset of data (2001–2010) and found that the percent of households with Aedes aegypti immatures was also a significant predictor. Our results indicate that monitoring the climate and non-climate drivers identified in this study could provide some predictive lead for forecasting dengue epidemics, showing the potential to develop a dengue early-warning system in this region.
Financial support: A.M.S.-I. received partial support from a Fulbright Institute of International Education fellowship. R.L. received partial funding from European Union Projects Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI) Grant 243964 and Dengue Research Framework for Resisting Epidemics in Europe (DENFREE) Grant 282 378 funded by the European Commission's Seventh Framework Research Programme.
Authors' addresses: Anna M. Stewart-Ibarra, Center for Global Health and Translational Science, State University of New York Upstate Medical University, Syracuse, NY, E-mail: firstname.lastname@example.org. Rachel Lowe, The Catalan Institute of Climate Sciences (IC3), Barcelona, Spain, E-mail: email@example.com.