Critical review of research literature on climate-driven malaria epidemics in sub-Saharan Africa
SOURCE: Public Health
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2012
TITLE AUTHOR(S): M.L.H.Mabaso, N.C.Ndlovu
KEYWORDS: MALARIA, MALARIA EARLY WARNING SYSTEM (MEWS), RESEARCH, SUB-SAHARAN AFRICA
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 7470
HANDLE: 20.500.11910/3212
URI: http://hdl.handle.net/20.500.11910/3212
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2012
TITLE AUTHOR(S): M.L.H.Mabaso, N.C.Ndlovu
KEYWORDS: MALARIA, MALARIA EARLY WARNING SYSTEM (MEWS), RESEARCH, SUB-SAHARAN AFRICA
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 7470
HANDLE: 20.500.11910/3212
URI: http://hdl.handle.net/20.500.11910/3212
If you would like to obtain a copy of this Research Output, please contact Hanlie Baudin at researchoutputs@hsrc.ac.za.
Abstract
Objectives: To obtain a better understanding of existing research evidence towards the development of climate-driven malaria early warning systems (MEWS) through critical review of published literature in order to identify challenges and opportunities for future research. Study Design: Literature review. Methods: A comprehensive search of English literature published between 1990 and 2009 was conducted using the electronic bibliographic database, PubMed. Only studies that explored the associations between environmental and meteorological covariates, El Nino Southern Oscillation (ENSO) and malaria as the basis for developing, testing or implementing MEWS were considered. Results: In total, 35 relevant studies revealed that the development of functional climate based MEWS remains a challenge, partly due to the complex web of causality and partly due to the use of imprecise malaria data, spatially and temporally varying covariate data, and different analytical approaches with divergent underlying assumptions. Nevertheless, high resolution spatial and temporal data, innovative analytical tools, and new and automated approaches for early warning and the development of operational MEWS. Conclusions: Future research should exploit these opportunities and incorporate the various aspects of MEWS for functional epidemic forecasting systems to be realized.-
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