Next Article in Journal
Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter
Next Article in Special Issue
Spatial Distribution, Sources Apportionment and Health Risk of Metals in Topsoil in Beijing, China
Previous Article in Journal
BMI, Waist Circumference Reference Values for Chinese School-Aged Children and Adolescents
Previous Article in Special Issue
Measurement and Study of Lidar Ratio by Using a Raman Lidar in Central China
Article Menu

Export Article

Open AccessReview
Int. J. Environ. Res. Public Health 2016, 13(6), 584;

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4000, South Africa
Author to whom correspondence should be addressed.
Academic Editor: Jamal Jokar Arsanjani
Received: 8 April 2016 / Revised: 2 June 2016 / Accepted: 8 June 2016 / Published: 14 June 2016
Full-Text   |   PDF [1069 KB, uploaded 17 June 2016]   |  


Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. View Full-Text
Keywords: remote sensing; climatic/environmental variables; predictors; epidemiology; Sub-Saharan Africa remote sensing; climatic/environmental variables; predictors; epidemiology; Sub-Saharan Africa

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ebhuoma, O.; Gebreslasie, M. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa. Int. J. Environ. Res. Public Health 2016, 13, 584.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top