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

Bibliographic Data

ID15500992
AuthorsKarina Lalangui (0000-0001-8506-4107, Universidad UTE, corresponding author), Emmanuelle Quentin (0000-0001-5600-2361, Universidad UTE, corresponding author), Marco Sánchez-Murillo (0000-0002-3719-8589, Instituto Nacional de Investigación en Salud Pública), M C Sevilla Mantilla (0000-0002-1560-2130, Instituto Nacional de Investigación en Salud Pública), Luis Loor (0000-0002-8267-7351, Universidad Laica Eloy Alfaro de Manabí), Milton René Espinoza Lucas (0000-0002-6945-660X, Universidad Laica Eloy Alfaro de Manabí), Johanna Mabel Sánchez Rodríguez (0000-0002-7799-1151, Universidad Laica Eloy Alfaro de Manabí), Mauricio Espinel (Universidad Laica Eloy Alfaro de Manabí), Patricio Ponce (0000-0002-7627-1056, Instituto Nacional de Investigación en Salud Pública), Varsovia Cevallos (0000-0001-8666-1536, Instituto Nacional de Investigación en Salud Pública)
Year2025
Volume22
Issue10
Pages1521-1521
Publication date2025-10-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph22101521
PMID41154925
OpenAlexW4414847965
LanguageEN
References cited11

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

Dengue fever · Distribution (mathematics · Geographic information system · Georeference · Population · Socioeconomic status · Spatial distribution · Zoning · COVID-19 epidemiological studies · Mosquito-borne diseases and control · Species Distribution and Climate Change

  • A statistical explanation of MaxEnt for ecologists

    Open Access•Jane Elith, Steven J Phillips et al.•Diversity and Distributions•2011

  • Maximum entropy modeling of species geographic distributions

    Open Access•Steven J Phillips, Robert P Anderson et al.•Ecological Modelling•2006

Citation velocityhistorical
Highly citedNo
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