Spatial pattern evolution of Aedes aegypti breeding sites in an Argentinean city without a dengue vector control programme.

The main objective of this study was to obtain and analyse the space-time dynamics of Aedes aegypti breeding sites in Clorinda City, Formosa Province, Argentina coupled with landscape analysis using the maximum entropy approach in order to generate a dengue vector niche model. In urban areas, without vector control activities, 12 entomologic (larval) samplings were performed during three years (October 2011 to October 2014). The entomologic surveillance area represented 16,511 houses. Predictive models for Aedes distribution were developed using vector breeding abundance data, density analysis, clustering and geoprocessing techniques coupled with Earth observation satellite data. The spatial analysis showed a vector spatial distribution pattern with clusters of high density in the central region of Clorinda with a well-defined high-risk area in the western part of the city. It also showed a differential temporal behaviour among different areas, which could have implications for risk models and control strategies at the urban scale. The niche model obtained for Ae. aegypti, based on only one year of field data, showed that 85.8% of the distribution of breeding sites is explained by the percentage of water supply (48.2%), urban distribution (33.2%), and the percentage of urban coverage (4.4%). The consequences for the development of control strategies are discussed with reference to the results obtained using distribution maps based on environmental variables.

Temporal Dynamics and Spatial Patterns of Aedes aegypti Breeding Sites, in the Context of a Dengue Control Program in Tartagal (Salta Province, Argentina).

BACKGROUND: Since 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 FINDINGS: Seasonal 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.

An operative dengue risk stratification system in Argentina based on geospatial technology.

Based on an agreement between the Ministry of Health and the National Space Activities Commission in Argentina, an integrated informatics platform for dengue risk using geospatial technology for the surveillance and prediction of risk areas for dengue fever has been designed. The task was focused on developing stratification based on environmental (historical and current), viral, social and entomological situation for >3,000 cities as part of a system. The platform, developed with open-source software with pattern design, following the European Space Agency standards for space informatics, delivers two products: a national risk map consisting of point vectors for each city/town/locality and an approximate 50 m resolution urban risk map modelling the risk inside selected high-risk cities. The operative system, architecture and tools used in the development are described, including a detailed list of end users' requirements. Additionally, an algorithm based on bibliography and landscape epidemiology concepts is presented and discussed. The system, in operation since September 2011, is capable of continuously improving the algorithms producing improved risk stratifications without a complete set of inputs. The platform was specifically developed for surveillance of dengue fever as this disease has reemerged in Argentina but the aim is to widen the scope to include also other relevant vector-borne diseases such as chagas, malaria and leishmaniasis as well as other countries belonging to south region of Latin America.

Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotely-sensed data.

In response to the first human outbreak (January May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.