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

TL;DR

This study analyzed the spatial, temporal, climatic, and social-ecological factors influencing the 2010 dengue outbreak in Machala, Ecuador, revealing hotspots, risk factors, and climate patterns that can inform early warning systems.

Contribution

It integrates geospatial, climatic, and social data to identify key risk factors and climate patterns associated with dengue outbreaks, advancing predictive modeling for disease control.

Findings

Dengue hotspots were identified near Machala's center.

Risk factors include household age, gender, water access, and housing quality.

Dengue transmission co-varied with rainfall and temperature annually and biannually.

Abstract

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.