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 informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be empirically interpreted for use in vector control activities. INDEX TERMS Ae. aegypti, machine learning, random forest, remote sensing.

Predicting Aedes Aegypti Eggs Count Using Remote Sensing Data and a Generalized Linear Model

“…In this work, the rounded-up mean Ae. aegypti eggs count (Y ) 1 is modeled as a function of environmental variables (X) derived from RS data. For this reason, a Poisson GLM with a logarithm link function is used.…”

“…As a result, it has been applied in many domains. One of such domains is landscape epidemiology which focuses on using remotely sensed data information to understand and model the dynamics of environment-dependent disease risk [1,2,3]. Traditionally, in order to model health-related risk proxies in urban areas, post-hoc outbreak evaluations were performed.…”

“…This approach, however, is only effective a posteriori. For prevention purposes, monitoring of vector dynamics and disease spread should be performed ahead of outbreak events [1]. Accordingly, there have been multiple attempts in landscape epidemiology towards implementing remote sensing-based systems to predict or forecast, for instance, the oviposition activity and adult population of mosquito species which are known vectors of widespread diseases [3,4,5].…”

Long-Range Dispersal Behaviour and Spatial Distribution Modelling of Adult Mosquitoes in the Winnipeg Region

“…Many similar studies have used NDWI as a predictor of mosquito activity (Zou et al 2006;Brown et al 2008;Estallo et al 2018;Mudele et al 2019;Piedrahita et al 2020). However, it is important to note that all use another index by the same name proposed by Gao (1996) Eid et al 2020) may be helpful in identifying similar adult mosquito habitats.…”

“…Random Forest model predictions will tend towards the value with highest variation in the response variable Therefore it is sometimes advisable to transform the response variable to improve its distribution (De'Ath and Fabricius 2000; Ibañez-Justicia and Cianci 2015). Another caveat to its use and interpretation is that RF cannot extrapolate, it can only interpolate(Hengl et al 2018), and so the minimum and maximum prediction values will always reflect those in the training data.When modelling species distribution, including that of mosquitoes, RF has been shown by many to outperform traditional parametric regression methods, including competing machine learning approaches(Li and Wang 2013;Kwon et al 2015;Mi et al 2017;Diarra et al 2018;Mudele et al 2019;Uusitalo et al 2019). However, only recently has the use of RF become more common in mosquito distribution studies (Ibañez-Justicia and Cianci 2015;Kwon et al 2015;Ferraguti et al 2016;Ong et al 2018;Chen et al 2019).…”