ArticlePLoS neglected tropical diseases2016
Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina).
Article in PLoS neglected tropical diseases, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Trends in mosquito species distribution modeling: insights for vector surveillance and disease control.Parasites & vectors · 2023Pooled it
- A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.International journal of environmental research and public health · 2022Pooled it
- Predicting the ecological niches ofFrontiers in tropical diseases · 2025Article
- Designing a multidimensional vulnerability index for supervising dengue cases from 2015 to 2020 in a low/middle-income country: A spatial principal component analysis.PLoS neglected tropical diseases · 2025Observational
- Infestation and Larval Habitat Ecology ofInsects · 2025Article
- Spatial autocorrelation of environmental factors influencing dengue outbreaks using Moran's I: A study from Nepal (2020-2023).PloS one · 2025Article
- Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.International journal of health geographics · 2024Article
- Risk Stratification to Guide Prevention and Control Strategies for Arboviruses Transmitted byTropical medicine and infectious disease · 2023Article
- How heterogeneous is the dengue transmission profile in Brazil? A study in six Brazilian states.PLoS neglected tropical diseases · 2022Article
- Spatial Distribution ofInsects · 2021Article
- Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China.Parasites & vectors · 2021Article
- Predicting Aedes aegypti infestation using landscape and thermal features.Scientific reports · 2020Article
- Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth Observation.International journal of environmental research and public health · 2020Review
- Large scale detailed mapping of dengue vector breeding sites using street view images.PLoS neglected tropical diseases · 2019Article
- Article
- Solid Wastes Provide Breeding Sites, Burrows, and Food for Biological Disease Vectors, and Urban Zoonotic Reservoirs: A Call to Action for Solutions-Based Research.Frontiers in public health · 2019Review
- Applications of Space Technologies to Global Health: Scoping Review.Journal of medical Internet research · 2018Article
- Spatial and Temporal Characteristics of 2014 Dengue Outbreak in Guangdong, China.Scientific reports · 2018Article
- DETERMINATION OF ENVIRONMENTAL FACTORS AFFECTING DENGUE INCIDENCE IN SLEMAN DISTRICT, YOGYAKARTA, INDONESIA.African journal of infectious diseases · 2018Article
- Social-ecological factors and preventive actions decrease the risk of dengue infection at the household-level: Results from a prospective dengue surveillance study in Machala, Ecuador.PLoS neglected tropical diseases · 2017Article
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6 authors.
Funding
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Abstract
backgroundSince 2009, Fundación Mundo Sano has implemented an Aedes aegypti Surveillance and Control Program in Tartagal city (Salta Province, Argentina). The purpose of this study was to analyze temporal dynamics of Ae. aegypti breeding sites spatial distribution, during five years of samplings, and the effect of control actions over vector population dynamics. METHODOLOGY/PRINCIPAL
findingsSeasonal entomological (larval) samplings were conducted in 17,815 fixed sites in Tartagal urban area between 2009 and 2014. Based on information of breeding sites abundance, from satellite remote sensing data (RS), and by the use of Geographic Information Systems (GIS), spatial analysis (hotspots and cluster analysis) and predictive model (MaxEnt) were performed. Spatial analysis showed a distribution pattern with the highest breeding densities registered in city outskirts. The model indicated that 75% of Ae. aegypti distribution is explained by 3 variables: bare soil coverage percentage (44.9%), urbanization coverage percentage(13.5%) and water distribution (11.6%). CONCLUSIONS/SIGNIFICANCE: This results have called attention to the way entomological field data and information from geospatial origin (RS/GIS) are used to infer scenarios which could then be applied in epidemiological surveillance programs and in the determination of dengue control strategies. Predictive maps development constructed with Ae. aegypti systematic spatiotemporal data, in Tartagal city, would allow public health workers to identify and target high-risk areas with appropriate and timely control measures. These tools could help decision-makers to improve health system responses and preventive measures related to vector control.
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