Dengue Vector Population Forecasting Using Multisource Earth Observation Products and Recurrent Neural Networks

Abstract

This article introduces a technique for using recurrent neural networks to forecast Ae. aegyptimosquito (Dengue transmission vector) counts at neighborhood-level, using Earth Observation data inputs as proxies to environmental variables. The model is validated using in situdata in two Brazilian cities, and compared with state-of-the-art multioutput random forest and k-nearest neighbor models. The approach exploits a clustering step performed before the model definition, which simplifies the task by aggregating mosquito count sequences with similar temporal patterns.

Keywords

• Deep learning
• dengue risk
• remote sensing
• satellite images
• Aedes aegypti%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Aedes aegypti</italic>