Modeling the Temporal Population Distribution of Ae. aegypti Mosquito
using Big Earth Observation Data.
Abstract
Over 50% of the world population is at risk of mosquito-borne diseases.
Female Ae. aegypti mosquito species transmit Zika, Dengue, and
Chikungunya. The spread of these diseases correlate positively with the
vector population, and this population depends on biotic and abiotic
environmental factors including temperature, vegetation condition,
humidity and precipitation. To combat virus outbreaks, information about
vector population is required. To this aim, Earth observation (EO) data
provide fast, efficient and economically viable means to estimate
environmental features of interest. In this work, we present a temporal
distribution model for adult female Ae. aegypti mosquitoes based on the
joint use of the Normalized Difference Vegetation Index, the Normalized
Difference Water Index, the Land Surface Temperature (both at day and
night time), along with the precipitation information, extracted from EO
data. The model was applied separately to data obtained during three
different vector control and field data collection condition regimes,
and used to explain the differences in environmental variable
contributions across these regimes. To this aim, a random forest (RF)
regression technique and its nonlinear features importance ranking based
on mean decrease impurity (MDI) were implemented. To prove the
robustness of the proposed model, other machine learning techniques,
including support vector regression, decision trees and k-nearest
neighbor regression, as well as artificial neural networks, and
statistical models such as the linear regression model and generalized
linear model were also considered. Our results show that machine
learning techniques perform better than linear statistical models for
the task at hand, and RF performs best. By ranking the importance of all
features based on MDI in RF and selecting the subset comprising the most