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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.

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This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weather variables (rainfall, photoperiod, vapor pressure of water, temperature, and relative humidity) with and without time lags (1 to 6 weeks). A stepwise multiple linear regression analysis was performed with the set of meteorological variables from the first year of study with the variables in the time lags that best correlated with the oviposition. Model validation was conducted using the data from the second year of study (October 2006- 2007). Minimum temperature and rainfall were the most important variables. No eggs were found at temperatures below 10°C. The most significant time lags were 3 weeks for minimum temperature and rains, 3 weeks for water vapor pressure, and 6 weeks for maximum temperature. Aedes aegypti could be expected in Orán three weeks after rains with adequate min temperatures. The best-fit forecasting model for the combined meteorological variables explained 70 % of the variance (adj. R2). The correlation between Ae. aegypti oviposition observed and estimated by the forecasting model resulted in rs = 0.80 (P < 0.05). The forecasting model developed would allow prediction of increases and decreases in the Ae. aegypti oviposition activity based on meteorological data for Orán city and, according to the meteorological variables, vector activity can be predicted three or four weeks in advance.

Dengue has affected the north provinces of Argentina, mainly Salta province. The 2009 outbreak, with 5 deaths and >27,000 infected, was the most important, and the first to extend into the central area of the country. This article includes research on seasonal Aedes aegypti abundance variation in Orán City (Salta province), and determination of the date of mosquito population increase and an estimation of the date of maximum rate of increase as well as the intrinsic rate of natural increase (r), to detect the optimal time to apply vector control measures. Between September 2005 and March 2007, ovitraps were randomly distributed in the city to collect Ae. aegypti eggs. The variation observed in the number of collected eggs was described by fitting a third-degree polynomial by the least square method, allowing to determine the time when population increase began (week 1), after the temperate and dry season. Eggs were collected throughout the year, with the highest variation in abundance during the warm and rainy season, and the maximum value registered in February 2007. The rate of increase of the number of eggs laid per week peaked between weeks 9 and 10 after the beginning of the population increase (week 1). Week 1 depends on temperature, it occurs after getting over the thermal threshold and the needed accumulation of 160 degree-day is reached. Consequently, week 1 changes depending on temperature. Peak abundance of eggs during 2005-2006 was recorded on week 15 (after week 1); during 2006-2007, the peak was observed on week 22. Estimation of the intrinsic rate of natural increase (r) of Ae. aegypti is useful not only to determine optimal time to apply vector control measures with better cost-benefit, but also to add an insecticide control strategy against the vector to diminish the possibility of resistance.

BackgroundIn Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively.MethodologyOviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005–2007). Spatial autocorrelation was measured with Moran’s Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity.Principal FindingsBoth cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC = 0.77), obtaining 99% of sensitivity and 75.29% of specificity.ConclusionsDistance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

The study aimed to determinate the maximum daily peak of Aedes aegypti oviposition in the city of Oran, northwestern Argentina. Biweekly samplings were taken between November 2006 and February 2007 (spring-summer). The city was divided into three areas (north, center, and south) and households were randomly selected. Two ovitraps were placed outdoors in the selected houses. Ovitraps were replaced every four hours, from morning (8 a.m.) to late afternoon (8 p.m.). The largest number of eggs was recorded between 4 p.m. and 8 p.m. (81%). These fi ndings enhance our understanding of the vector and thus its control such as spraying during the hours of peak oviposition activity.