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.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Map of Ecuador: The approximate location of the study community is indicated by the star.

This map was constructed using shapefiles downloaded from https://data.humdata.org/, which is available under a CC BY-IGO license (https://data.humdata.org/dataset/ecuador-admin-level-2-boundaries).

Fig 2. Community Map.

The study community. 2a. Neighborhoods are coded by the reported ethnicity of the occupants. ‘Field’ represents the football field that represented a spatial risk factor in the outbreak. 2b. The red circle in indicates the part of the community where Ae. Aegypti were identified. For privacy reasons, we do not identify specific households where dengue cases occurred, or where Ae. Aegypti were identified. These maps were constructed de novo by the study team.

Fig 3. Cases identified by reported date of fever onset: Cases identified during the 2019 calendar year.

Suspected index cases are represented by triangles, while adjacent cases (one from a person living in a nearby community, and one from a person diagnosed after returning from a trip to the village) are represented by square. From Jan 1st to May 31st, case detection was based on Ministry of Health (MOH) clinical records, from Jun 1st to December 1st, case detection was based of combined MOH and community-based active surveillance.

Fig 4. Outdoor rain barrels: A photo of an outdoor rain barrel.

Fig 5

UAV images: Larger, outdoor potential larval habitats such as cisterns and rain barrels can be identified from UAV images (Fig 5A and 5B).