Operational satellite-based temporal modelling of Aedes population in Argentina
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...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/103156 |
| Acceso en línea: | http://hdl.handle.net/11336/103156 |
| Access Level: | acceso abierto |
| Palabra clave: | AEDES AEGYPTI ARGENTINA DENGUE MODELLING REMOTE SENSING https://purl.org/becyt/ford/3.3 https://purl.org/becyt/ford/3 |
| Sumario: | 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. |
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