Modeling Dengue vector population using remotely sensed data and machine learning

Acta Tropica
Juan M ScavuzzoAlejandro C Frery

Abstract

Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images. We use are NDVI, NDWI, LST night, LST day and TRMM-GPM rain from 2012 to 2016 as predictive variables. In contrast to previous works which use linear models, we employ Machine Learning techniques using completely accessible open source toolkits. These models have the advantages of being non-parametric and capable of describing nonlinear relationships between variables. Specifically, in addition to two linear approaches, we assess a support vector machine, an artificial neural networks, a K-nearest neighbors and a decision tree regr...Continue Reading

Citations

Dec 8, 2019·International Journal of Environmental Research and Public Health·Bipin Kumar AcharyaHualiang Lin
Jun 27, 2020·International Journal of Environmental Research and Public Health·Zhichao LiPeng Gong
Dec 18, 2020·PLoS Neglected Tropical Diseases·Jannelle CouretMarco Alvarez
Mar 20, 2020·Trends in Parasitology·Karthikeyan ChandrasegaranClément Vinauger
May 1, 2021·International Journal of Environmental Research and Public Health·Oliver Mendoza-CanoEfrén Murillo-Zamora
Apr 25, 2021·Mathematical Biosciences and Engineering : MBE·Vitaliy Yakovyna, Natalya Shakhovska
Sep 26, 2020·Journal of Biomedical Informatics·Matheus Henrique Dal Molin RibeiroLeandro Dos Santos Coelho

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