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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Aedes aegypti is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a main threat for our region. Taking into account this situation, several efforts have been done to use remote sensing to support public health decision making. Moderate resolution imaging spectroradiometer (MODIS) sensor provides moderate-resolution remote sensing products; therefore, we explore the application of MODIS products to vector-borne disease problems in Argentina. We develop temporal forecasting models of Ae. aegypti oviposition, and we include its validation and its application to the 2016 Dengue outbreak. Temporal series (10/2005 to 09/2007) from MODIS products of normalized difference vegetation index and diurnal land surface temperature were built. Two linear regression models were developed: model 1 which uses environmental variables with time lag and model 2 uses environmental variables without time lags. Model 2 was the best model (AIC = 112) with high correlation (r = 0.88, p < 0.05) between observed and predicted data. We can suggest that MODIS products could be a good tool for estimating both Ae. aegypti oviposition activity and risks for Ae. aegypti-borne diseases. That statement is also supported by model results for 2016 when a dengue outbreak that started unusually earlier this season. If such activity could be forecast by a model based on remote sensing data, then a potential outbreak could be predicted.

After elimination of the Aedes aegypti vector in South America in the 1960s, dengue outbreaks started to reoccur during the 1990s; strongly in Argentina since 1998. In 2016, Córdoba City had the largest dengue outbreak in its history. In this article we report this outbreak including spatio-temporal analysis of cases and vectors in the city. A total of 653 dengue cases were recorded by the laboratory-based dengue surveillance system and georeferenced by their residential addresses. Case maps were generated from the epidemiological week 1 (beginning of January) to week 19 (mid-May). Dengue outbreak temporal evolution was analysed globally and three specific, high-incidence zones were detected using Knox analysis to characterising its spatio-temporal attributes. Field and remotely sensed data were collected and analysed in real time and a vector presence map based on the MaxEnt approach was generated to define hotspots, towards which the pesticide- based strategy was then targeted. The recorded pattern of cases evolution within the community suggests that dengue control measures should be improved.

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

In the present study, we achieved the susceptibility mapping to slope instability processes by the implementation of Analytic Hierarchy Process and Weighted Linear Combination methods, in the North Branch of Argentino Lake, Southern Patagonian Icefield. The strong retraction of the glaciers in the area has triggered paraglacial readjustments, producing instability processes that favor the generation of mass removal processes. The results obtained from optical satellite images show that the highest degrees of susceptibility (4 and 5) are located on the western slopes of the Upsala Channel, Bertacchi and Cono Tributary Glaciers, and the Moyano and Norte Valleys, respectively. These slopes coincide with the geographic location of previous events surveyed by the inventory of unstable areas of the zone. Low degrees of susceptibility are found on the downhill valleys, outcrops rock and glaciers. The Consistency Ratio was 0.069, indicating that being less than 0.1 the study is reliable. The study sheds light on the knowledge of slopes and valleys that are more susceptible to processes of instability in mountainous areas, which would make it possible to prevent possible hazards associated with these events.

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.

Aedes aegypti is a vector for Chikungunya, Dengue and Zika viruses in Latin America and is therefore a large public health problem for the region. For this reason, several inter-institutional and multidisciplinary efforts have been made to support vector control actions through the use of geospatial technologies. This study presents the development of an operational system for the application of free access to remotely sensed products capable of assessing the oviposition activity of Ae. aegypti in all of Argentina’s northern region with the specific aim to improve the current Argentine National Dengue risk system. Temporal modelling implemented includes remotely sensed variables like the normalized difference vegetation index, the normalized difference water index, day and night land surface temperature and precipitation data available from NASA’s tropical rainfall measuring mission and global precipitation measurement. As a training data set, four years of weekly mosquito oviposition data from four different cities in Argentina were used. A series of satellite-generated variables was built, downloading and resampling the these products both spatially and temporally. From an initial set of 41 variables chosen based on the correlation between these products and the oviposition series, a subset of 11 variables were preserved to develop temporal forecasting models of oviposition using a lineal multivariate method in the four cities. Subsequently, a general model was generated using data from the cities. Finally, in order to obtain a model that could be broadly used, an extrapolation method using the concept of environmental distance was developed. Although the system was oriented towards the surveillance of dengue fever, the methodology could also be applied to other relevant vector-borne diseases as well as other geographical regions in Latin America.