Spatial Modeling of the Potential Distribution of Dengue in the City of Manta, Ecuador

LALANGUI VIVANCO, KARINA DEL ROCÍO; QUENTIN, EMMANUELLE; Marco Sánchez-Murillo; Max Cotera-Mantilla; Luis Loor; Milton Espinoza; Johanna Mabel Sánchez-Rodríguez; Mauricio Espinel; Patricio Ponce; Varsovia Cevallos

doi:https://doi.org/10.3390/ijerph22101521

Spatial Modeling of the Potential Distribution of Dengue in the City of Manta, Ecuador

Journal

International Journal of Environmental Research and Public Health

ISSN

1660-4601

Date Issued

2025-10-04

Author(s)

LALANGUI VIVANCO, KARINA DEL ROCÍO

Facultad de Ciencias de la Salud Eugenio Espejo

QUENTIN, EMMANUELLE

Facultad de Ciencias de la Salud Eugenio Espejo

Marco Sánchez-Murillo

Max Cotera-Mantilla

Luis Loor

Milton Espinoza

Johanna Mabel Sánchez-Rodríguez

Mauricio Espinel

Patricio Ponce

Varsovia Cevallos

DOI

https://doi.org/10.3390/ijerph22101521

URL

https://cris.ute.edu.ec/handle/123456789/2002

Abstract

In Ecuador, the transmission of dengue has steadily increased in recent decades, particularly in coastal cities like Manta, where the conditions are favorable for the proliferation of the Aedes aegypti mosquito. The objective of this study was to model the spatial distribution of dengue transmission risk in Manta, a coastal city in Ecuador with consistently high incidence rates. A total of 148 georeferenced dengue cases from 2018 to 2021 were collected, and environmental and socioeconomic variables were incorporated into a maximum entropy model (MaxEnt). Additionally, climate and social zoning were performed using a multi-criteria model in TerrSet. The MaxEnt model demonstrated excellent predictive ability (training AUC = 0.916; test AUC = 0.876) and identified population density, sewer system access, and distance to rivers as the primary predictors. Three high-risk clusters were identified in the southern, northwestern, and northeastern parts of the city, while the coastal strip showed lower suitability due to low rainfall and vegetation. These findings reveal the strong spatial heterogeneity of dengue risk at the neighborhood level and provide operational information for targeted interventions. This approach can support more efficient surveillance, resource allocation, and community action in coastal urban areas affected by vector-borne diseases.