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Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

Author

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  • Abiodun M. Adeola

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa)

  • Joel O. Botai

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
    Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Hannes Rautenbach

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa
    School for Health Systems and Public Health, University of Pretoria, Pretoria 0002, South Africa)

  • Omolola M. Adisa

    (Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)

  • Katlego P. Ncongwane

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa)

  • Christina M. Botai

    (South African Weather Service, Private Bag X097, Pretoria 0001, South Africa)

  • Temitope C. Adebayo-Ojo

    (School for Health Systems and Public Health, University of Pretoria, Pretoria 0002, South Africa)

Abstract

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature ( R 2 = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Suggested Citation

  • Abiodun M. Adeola & Joel O. Botai & Hannes Rautenbach & Omolola M. Adisa & Katlego P. Ncongwane & Christina M. Botai & Temitope C. Adebayo-Ojo, 2017. "Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis," IJERPH, MDPI, vol. 14(11), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:11:p:1360-:d:118029
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    References listed on IDEAS

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    1. Arne Bomblies, 2012. "Modeling the role of rainfall patterns in seasonal malaria transmission," Climatic Change, Springer, vol. 112(3), pages 673-685, June.
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    Cited by:

    1. Makwelantle Asnath Sehlabana & Daniel Maposa & Alexander Boateng, 2020. "Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
    2. Samuel Kwasi Opoku & Walter Leal Filho & Fudjumdjum Hubert & Oluwabunmi Adejumo, 2021. "Climate Change and Health Preparedness in Africa: Analysing Trends in Six African Countries," IJERPH, MDPI, vol. 18(9), pages 1-29, April.
    3. Eric Kalunda Panzi & Ngianga II Kandala & Emery Luzolo Kafinga & Bertin Mbenga Tampwo & Ngianga-Bakwin Kandala, 2022. "Forecasting Malaria Morbidity to 2036 Based on Geo-Climatic Factors in the Democratic Republic of Congo," IJERPH, MDPI, vol. 19(19), pages 1-28, September.
    4. Eric Kalunda Panzi & Léon Ngongo Okenge & Eugénie Hamuli Kabali & Félicien Tshimungu & Angèle Keti Dilu & Felix Mulangu & Ngianga-Bakwin Kandala, 2022. "Geo-Climatic Factors of Malaria Morbidity in the Democratic Republic of Congo from 2001 to 2019," IJERPH, MDPI, vol. 19(7), pages 1-16, March.
    5. Abiodun Adeola & Katlego Ncongwane & Gbenga Abiodun & Thabo Makgoale & Hannes Rautenbach & Joel Botai & Omolola Adisa & Christina Botai, 2019. "Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    6. Nicholas Ngepah & Regina Conselho Mwiinga, 2022. "The Impact of Climate Change on Gender Inequality in the Labour Market: A Case Study of South Africa," Sustainability, MDPI, vol. 14(20), pages 1-28, October.
    7. Muhammad Farooq Umer & Shumaila Zofeen & Abdul Majeed & Wenbiao Hu & Xin Qi & Guihua Zhuang, 2019. "Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis," IJERPH, MDPI, vol. 16(8), pages 1-12, April.
    8. Jiangtao Liu & Yueling Ma & Yuhong Wang & Sheng Li & Shuyu Liu & Xiaotao He & Lanyu Li & Lei Guo & Jingping Niu & Bin Luo & Kai Zhang, 2019. "The Impact of Cold and Heat on Years of Life Lost in a Northwestern Chinese City with Temperate Continental Climate," IJERPH, MDPI, vol. 16(19), pages 1-13, September.
    9. Gbenga J. Abiodun & Olusola S. Makinde & Abiodun M. Adeola & Kevin Y. Njabo & Peter J. Witbooi & Ramses Djidjou-Demasse & Joel O. Botai, 2019. "A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa," IJERPH, MDPI, vol. 16(11), pages 1-19, June.
    10. Matthew F. Chersich & Caradee Y. Wright & Francois Venter & Helen Rees & Fiona Scorgie & Barend Erasmus, 2018. "Impacts of Climate Change on Health and Wellbeing in South Africa," IJERPH, MDPI, vol. 15(9), pages 1-14, August.
    11. João L. Ferrão & Dominique Earland & Anísio Novela & Roberto Mendes & Alberto Tungadza & Kelly M. Searle, 2021. "Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique," IJERPH, MDPI, vol. 18(11), pages 1-16, May.

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