An operative dengue risk stratification system in Argentina based on geospatial technology
AbstractBased 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.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2012 Ximena Porcasi, Camilo H. Rotela, María V. Introini, Nicolás Frutos, Sofía Lanfri, Gonzalo Peralta, Estefanía A. De Elia, Mario A. Lanfri, Carlos M. Scavuzzo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.