Abstract
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.
Highlights
Malaria remains the number one killer of all infectious diseases in Sub-Saharan Africa (SSA) [1].In 2013, an estimated 198 million malaria cases and 584,000 malaria deaths were recorded
The study area(s), malaria case data, climatic variables and their sources, the statistical methods used and the main findings are provided for each study in Tables 1–4, while Table 5 provides an overview of the Remote sensing (RS) variables commonly used in SSA, and Table 6 provides the characteristics of the satellites/sensors used in the selected articles
We conclude that RS technology is a vital tool in determining malaria risk predictors at regional, national and local scales in diverse regions of SSA
Summary
Malaria remains the number one killer of all infectious diseases in Sub-Saharan Africa (SSA) [1]. In 2013, an estimated 198 million malaria cases and 584,000 malaria deaths were recorded. Of the malaria deaths recorded were from the SSA region [2]. Out of all known malaria parasites, viz. Plasmodium falciparum, P. vivax, P. ovale, P. malariae and P. knowlesi [2], P. falciparum is the most prevalent of the human malaria parasites in SSA, while the P. vivax malaria parasite is more common across the. Horn of Africa [3]. The spatial and temporal variation of malaria disease is known to be influenced by socio-economic/human, ecological/environmental and climatic factors [4,5].
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