A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping

Abstract

Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2–3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.

Validity of Self-Reported Mosquito Bites to Assess Household Mosquito Abundance in Six Communities of Esmeraldas Province, Ecuador

“…Data were collected between February 2021 and July 2022 from six communities in the Esmeraldas province of Ecuador. [18][19][20] These communities were selected to provide variation in population size and relative accessibility, with one larger town (Borb on), three smaller, road-accessible communities (Maldonado, Colon Eloy, and Timbir e), and two rural communities accessible only by river (Santo Domingo and Santa Maria). Around 30% of households in each town were randomly selected for inclusion in the study based on a previously conducted community census (Supplemental Table 1).…”

Evolution and emergence of mosquito-borne viruses of medical importance: towards a routine metagenomic surveillance approach

“…The historical circulation of DENV, and the recent re-emergence and establishment of CHIKV and ZIKV in the urban cycle in the Americas represent a potential risk for a new sylvatic cycle to be established (Althouse et al 2016;Figueiredo 2019). Human outbreaks, as well as the natural infection of urban and sylvatic mosquitos in peri-urban and rural settings have been documented (Alencar et al 2021;Catenacci et al 2021;Hoyos-López et al 2019;Lee et al 2021).…”

Automatic mapping of high-risk urban areas forAedes aegyptiinfestation based on building facade image analysis

“…To allow better observation and understanding of the region, they used several images to cover the entire surroundings of the area. Lee et al [54] combined entomological and health-related data with information extracted from Unmanned Aerial Vehicle images (such as water containers, and green-red vegetation index) to identify high-risk rural areas of mosquito infestation. Liu et al [55] compared and combined environmental features extracted from street-level images using pre-trained networks with standard features (such as epidemical, meteorological, and sociodemographic variables) to create a machine learning model capable of performing weekly dengue forecasting.…”