Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

Abstract

Background: In many studies in the field of malaria, environmental factors have been acquired in single-time, multi-time or a short time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreak.Methods: In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with history of malaria prevalence had been estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles had been used over a seven-year period through the GEE. Environmental factors used in this study include NDVI and LST extracted from Landsat-8 satellite images, daily precipitation data from PERSIANN-CDR, soil moisture data from NASA-USDA Enhanced SMAP, ET data from MODIS sensor, and vegetation health indices included TCI and VCI extracted from MODIS sensors. All these parameters were extracted on a monthly average for seven years and, their results were fused at the decision level using majority voting method to estimate high-risk time in a year.Results: The results of this study indicated that there were two high-risk times for all three study areas in a year to increase the abundance of Anopheles mosquitoes. The first peak occurred from late winter to late spring and the second peak from late summer to mid-autumn. If there is a malaria patient in the area, after the end of the Anopheles larvae growth period, the disease will spread throughout the region. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with the increase in the abundance of Anopheles mosquitoes in the study areas. Conclusions: The proposed method is very useful for temporal prediction of the increase of the abundance of Anopheles mosquitoes and also the use of optimal data with the aim of monitoring the exact location of Anopheles habitats. This study extracted high-risk time based on the analysis of the time series of remote sensing data.