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The objective of this study was to characterize the spatiotemporal dynamicsand climatic and social-ecological risk factors associated with the largestdengue epidemic to date in Machala, Ecuador, to inform the development of adengue EWS. The following data were included in analyses: neighborhood-levelgeoreferenced dengue cases, national census data, and entomologicalsurveillance data from 2010; time series of weekly dengue cases (aggregated tothe city-level) and meteorological data from 2003 to 2012. We applied LISA andMorans I to analyze the spatial distribution of the 2010 dengue cases, anddeveloped multivariate logistic regression models through a multi-modelselection process to identify census variables and entomological covariatesassociated with the presence of dengue at the neighborhood level. Using dataaggregated at the city-level, we conducted a time-series (wavelet) analysis ofweekly climate and dengue incidence (2003-2012) to identify significant timeperiods (e.g., annual, biannual) when climate co-varied with dengue, and todescribe the climate conditions associated with the 2010 outbreak. We foundsignificant hotspots of dengue transmission near the center of Machala. Thebest-fit model to predict the presence of dengue included older age and femalegender of the head of the household, greater access to piped water in the home,poor housing condition, and less distance to the central hospital. Waveletanalyses revealed that dengue transmission co-varied with rainfall and minimumtemperature at annual and biannual cycles, and we found that anomalously highrainfall and temperatures were associated with the 2010 outbreak. Our findingshighlight the importance of geospatial information in dengue surveillance andthe potential to develop a climate-driven spatiotemporal prediction models toinform disease prevention and control interventions.

Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010