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Observación de la Tierra para la modelización de la malaria: herramientas prácticas para la predicción de la distribución de mosquitos utilizando datos de satélite, Google Earth Engine y R

Authors: Marston, Christopher G.; Rowland, Clare S.; O'Neil, Aneurin W.; Irish, Seth; Wat'senga, Francis; Martin-Gallego, Pilar; Giraudoux, Patrick; +1 Authors

Observación de la Tierra para la modelización de la malaria: herramientas prácticas para la predicción de la distribución de mosquitos utilizando datos de satélite, Google Earth Engine y R

Abstract

Esta guía de usuario proporciona una introducción sencilla al análisis basado en teledetección y la modelización usando Random Forest (bosques aleatorios) para identificar las variables biogeográficas clave que influyen en la distribución y abundancia de mosquitos en el contexto de los estudios sobre malaria. Pretende ser un recurso para usuarios con conocimientos previos limitados sobre este tipo de análisis y presenta instrucciones paso a paso para que los usuarios realicen la modelización predictiva de distribuciones de mosquitos. Se proporcionan conjuntos de datos de muestra y secuencias de comandos de análisis. Esta guía utiliza tres paquetes de software, Google Earth Engine, R (con RStudio) y QGIS para el preprocesamiento, modelado y visualización de datos, todos ellos gratuitos para uso no comercial. Se proporcionan scripts de ejemplo para realizar el procesamiento y análisis de datos tanto en Google Earth Engine (GEE) como en R, y aunque estos scripts están diseñados para automatizar el análisis en gran medida, actualmente están optimizados para el área de estudio y el período de tiempo utilizados en el ejemplo trabajado para Lodja, República Democrática del Congo. Los usuarios son libres de adaptarlos a sus propios fines. Siguiendo las instrucciones, el usuario aprenderá a aplicar estos métodos. De este modo, los usuarios podrán aplicar estos métodos a diferentes conjuntos de datos, zonas y escenarios de su elección.

Keywords

Remote Sensing, Random Forests, Mosquito, Earth Observation, Anopheles gambiae, Google Earth Engine, Malaria

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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