Phlebotominae in Northwestern Argentina: preliminary Distribution Map model

QUINTANA MG, SALOMÓN OD, LIZARRALDE DE GROSSO MS

Lima, Peru

Simposio; ISOPS. 6th International Symposium On Phlebotomine Sandflies; 2008

In the last 20 years,  the evidence of an association between vector-borne diseases re-emergence and environmental changes increased. During the same period the cases of cutaneous leishmaniasis in Argentina rose associated with changes in vectors density or distribution, that facilitate their effective contact with humans. The re-emergence was explained mainly by: 1) deforestation (logging, expansion of the agricultural border), 2) unplanned urban growth (rural-periurban), 3) climate / environmental factors. In the last decade, the use of algorithms to model ecological niches of the species was used for several applications including eco-epidemiology. These models are applied to explore relationships between the values of biotics (type of vegetation) and abiotic factors (temperature, precipitation, etc), with data presence, presence-absence and / or abundance of species. In this study we developed a model to estimate the more uniform distribution of Phlebotominae in Argentinian Northwestern with the program MAXENT, with the aim of generating a map of potential distribution of vectors based on biotic and abiotic conditions, and abundance and distribution of vectors in different environments of the region, to contribute to design spatially focused strategies for surveillance and prevention of leishmaniasis. The study area is classified as subtropical humid forest, where the forest degradation has been taking place since the decade of 80. The model was developed with 98 entries of Phlebotominae, according to altitudinal and latitudinal gradients, watersheds, environmental changes and history of human cases. A total of 12.079 sand flies were captured, Lu.neivai (85,6%), Lu.migonei (11,6%), Lu.cortelezzii (2,6%) and Lu. shannoni, Lu.quinquefer y Brumptomyia spp. (less than 0,1%). The first two species had epidemiological importance because are vectors of Leishmania braziliensis. Human cases have not entered into the analysis because they introduce errors by asymptomatic incidence and bias on spatial data obtained from clinical files. In the analysis were used seven layers of BIOBLIM: annual precipitation, maximum, minimum and seasonal temperature, and elevation. The variables with the highest relative contribution were: seasonal rainfall, seasonal temperature and elevation. This approach based on models gives us, once validated, the ability to predict and assess the impact of enviromental changes on public health, designing control strategies, and generate predictive models based on remote sensing and captures of vectors in sentinel sites, in order to provide an early warning system for outbreaks monitored in real time.