Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa

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dc.contributor.author Adeola, A.M. (Abiodun Morakinyo)
dc.contributor.author Botai, J.O. (Joel Ongego)
dc.contributor.author Olwoch, Jane Mukarugwiza
dc.contributor.author Rautenbach, C.J. de W. (Cornelis Johannes de Wet)
dc.contributor.author Adisa, O.M. (Omolola)
dc.contributor.author De Jager, Christiaan
dc.contributor.author Botai, M.C. (Mihloti Christina)
dc.contributor.author Aaron, Mabuza
dc.date.accessioned 2020-08-21T12:10:01Z
dc.date.available 2020-08-21T12:10:01Z
dc.date.issued 2019
dc.description AMA conceptualized and designed the research, analyzed the data and wrote the original draft paper. JOB, JMO and HCR supervised, reviewed and edited the manuscript. OMA, CMB and MA assisted in data collection and reviewing the paper. CDJ reviewed and edited the paper. en_ZA
dc.description.abstract There has been a conspicuous increase in malaria cases since 2016/2017 over the three malaria-endemic provinces of South Africa. This increase has been linked to climatic and environmental factors. In the absence of adequate traditional environmental/climatic data covering ideal spatial and temporal extent for a reliable warning system, remotely sensed data are useful for the investigation of the relationship with, and the prediction of, malaria cases. Monthly environmental variables such as the normalised difference vegetation index (NDVI), the enhanced vegetation index (EVI), the normalised difference water index (NDWI), the land surface temperature for night (LSTN) and day (LSTD), and rainfall were derived and evaluated using seasonal autoregressive integrated moving average (SARIMA) models with different lag periods. Predictions were made for the last 56 months of the time series and were compared to the observed malaria cases from January 2013 to August 2017. All these factors were found to be statistically significant in predicting malaria transmission at a 2-months lag period except for LSTD which impact the number of malaria cases negatively. Rainfall showed the highest association at the two-month lag time (r=0.74; P<0.001), followed by EVI (r=0.69; P<0.001), NDVI (r=0.65; P<0.001), NDWI (r=0.63; P<0.001) and LSTN (r=0.60; P<0.001). SARIMA without environmental variables had an adjusted R2 of 0.41, while SARIMA with total monthly rainfall, EVI, NDVI, NDWI and LSTN were able to explain about 65% of the variation in malaria cases. The prediction indicated a general increase in malaria cases, predicting about 711 against 648 observed malaria cases. The development of a predictive early warning system is imperative for effective malaria control, prevention of outbreaks and its subsequent elimination in the region. en_ZA
dc.description.department Geography, Geoinformatics and Meteorology en_ZA
dc.description.department School of Health Systems and Public Health (SHSPH) en_ZA
dc.description.department UP Centre for Sustainable Malaria Control (UP CSMC) en_ZA
dc.description.librarian am2020 en_ZA
dc.description.uri http://www.geospatialhealth.net/index.php/gh en_ZA
dc.identifier.citation Adeola, A.M., Botai, J.O., Olwoch, J.M. et al. 2019, 'Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa', Geospatial Health, vol. 14, no. 1, art 676, pp. 81-91. en_ZA
dc.identifier.issn 1827-1987 (print)
dc.identifier.issn 1970-7096 (online)
dc.identifier.other 10.4081/gh.2019.676
dc.identifier.uri http://hdl.handle.net/2263/75851
dc.language.iso en en_ZA
dc.publisher PAGEpress en_ZA
dc.rights © A.M. Adeola et al. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License (CC BY-NC 4.0). en_ZA
dc.subject Malaria en_ZA
dc.subject Environmental en_ZA
dc.subject Climatic en_ZA
dc.subject Remote sensing en_ZA
dc.subject Prediction en_ZA
dc.subject South Africa (SA) en_ZA
dc.subject Seasonal autoregressive integrated moving average (SARIMA) en_ZA
dc.subject Normalised difference vegetation index (NDVI) en_ZA
dc.subject Rainfall en_ZA
dc.subject Enhanced vegetation index (EVI) en_ZA
dc.subject Land surface temperature for day (LSTD) en_ZA
dc.subject Land surface temperature for night (LSTN) en_ZA
dc.subject Normalised difference water index (NDWI) en_ZA
dc.title Predicting malaria cases using remotely sensed environmental variables in Nkomazi, South Africa en_ZA
dc.type Article en_ZA


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