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

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

Youssefi F¹ ; Zoej MJV¹ ; Hanafibojd AA² ; Dariane AB³ ; Khaki M⁴ ; Safdarinezhad A⁵ ; Ghaderpour E⁶

Show Affiliations

1. Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
2. Department of Medical Entomology & Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran 6446-14155, Iran
3. Department of Civil Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
4. School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia
5. Department of Geodesy and Surveying Engineering, Tafresh University, Tafresh 79611-39518, Iran
6. Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada

Source: Sensors (Basel# Switzerland) Published:2022

Abstract

In many studies regarding 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 outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. 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 an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.

Style	Citing Format
MLA	Youssefi F, et al.. "Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine." Sensors (Basel# Switzerland), vol. 22, no. 5, 2022, pp. -.
APA	Youssefi F, Zoej MJV, Hanafibojd AA, Dariane AB, Khaki M, Safdarinezhad A, Ghaderpour E (2022). Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine. Sensors (Basel# Switzerland), 22(5), -.
Chicago	Youssefi F, Zoej MJV, Hanafibojd AA, Dariane AB, Khaki M, Safdarinezhad A, Ghaderpour E. "Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine." Sensors (Basel# Switzerland) 22, no. 5 (2022): -.
Harvard	Youssefi F et al. (2022) 'Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine', Sensors (Basel# Switzerland), 22(5), pp. -.
Vancouver	Youssefi F, Zoej MJV, Hanafibojd AA, Dariane AB, Khaki M, Safdarinezhad A, et al.. Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine. Sensors (Basel# Switzerland). 2022;22(5):-.
BibTex	@article{ author = {Youssefi F and Zoej MJV and Hanafibojd AA and Dariane AB and Khaki M and Safdarinezhad A and Ghaderpour E}, title = {Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine}, journal = {Sensors (Basel# Switzerland)}, volume = {22}, number = {5}, pages = {-}, year = {2022} }
RIS	TY - JOUR AU - Youssefi F AU - Zoej MJV AU - Hanafibojd AA AU - Dariane AB AU - Khaki M AU - Safdarinezhad A AU - Ghaderpour E TI - Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data Through Google Earth Engine JO - Sensors (Basel# Switzerland) VL - 22 IS - 5 SP - EP - PY - 2022 ER -

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