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.