  The objective of this study was to characterize the spatiotemporal dynamics
and climatic and social-ecological risk factors associated with the largest
dengue epidemic to date in Machala, Ecuador, to inform the development of a
dengue EWS. The following data were included in analyses: neighborhood-level
georeferenced dengue cases, national census data, and entomological
surveillance data from 2010; time series of weekly dengue cases (aggregated to
the city-level) and meteorological data from 2003 to 2012. We applied LISA and
Morans I to analyze the spatial distribution of the 2010 dengue cases, and
developed multivariate logistic regression models through a multi-model
selection process to identify census variables and entomological covariates
associated with the presence of dengue at the neighborhood level. Using data
aggregated at the city-level, we conducted a time-series (wavelet) analysis of
weekly climate and dengue incidence (2003-2012) to identify significant time
periods (e.g., annual, biannual) when climate co-varied with dengue, and to
describe the climate conditions associated with the 2010 outbreak. We found
significant hotspots of dengue transmission near the center of Machala. The
best-fit model to predict the presence of dengue included older age and female
gender of the head of the household, greater access to piped water in the home,
poor housing condition, and less distance to the central hospital. Wavelet
analyses revealed that dengue transmission co-varied with rainfall and minimum
temperature at annual and biannual cycles, and we found that anomalously high
rainfall and temperatures were associated with the 2010 outbreak. Our findings
highlight the importance of geospatial information in dengue surveillance and
the potential to develop a climate-driven spatiotemporal prediction models to
inform disease prevention and control interventions.
