Predictive Modeling of Malaria Cases in Beitbridge, Zimbabwe Using SARIMA

This study applies Seasonal Autoregressive Integrated Moving Average (SARIMA) models to predict malaria incidence in Beitbridge district, Zimbabwe. Using monthly malaria case data from 2015-2022, we developed, evaluated, and validated time series models to forecast malaria trends. The optimal SARIMA(2,1,1)(1,1,1)₁₂ model was identified through comprehensive diagnostic testing. Model validation showed strong predictive performance with a Mean Absolute Percentage Error (MAPE) of 12.7% and Root Mean Square Error (RMSE) of 18.6. Six-month forecasts revealed expected seasonal peaks in April-May with declining trends. These findings demonstrate SARIMA modeling's utility for malaria surveillance and can inform targeted intervention timing in resource-limited settings. This research provides evidence-based tools for enhancing malaria control strategies in Beitbridge and similar endemic regions.

Malaria, SARIMA, Time Series Analysis

1. Anwar, M. Y., Lewnard, J. A., Parikh, S., & Pitzer, V. E. (2021). Time series analysis of malaria in Bangladesh: Exploring the effects of climatic factors and population movement. Malaria Journal, 20(1), 175. [Google Scholar] [Crossref]

2. Ferrão, J. L., Mendes, J. M., Painho, M., & João, S. Z. (2017). Spatio-temporal variation and socio- demographic characters of malaria in Chimoio municipality, Mozambique. Malaria Journal, 16(1), 423. [Google Scholar] [Crossref]

3. Gwitira, I., Murwira, A., Mberikunashe, J., & Masocha, M. (2018). Spatial and spatio-temporal analysis of malaria cases in Zimbabwe. Infectious Diseases of Poverty, 7(1), 91. [Google Scholar] [Crossref]

4. Mabaso, M. L., Ngwenya, N. P., Mberikunashe, J., Gwitira, I., Saili, K., & Mudzengi, D. (2021). Malaria trends and correlates of malaria testing, treatment, and insecticide-treated nets utilization in Zimbabwe: Analysis of successive demographic and health surveys. Malaria Journal, 20(1), 356. [Google Scholar] [Crossref]

5. Tesfahunegn, A., Berhe, G., & Gebregziabher, E. (2020). Seasonal autoregressive integrated moving average (SARIMA) model for predicting monthly malaria cases in Adi-Yakeni hospital, Tigray, Ethiopia. International Journal of Infectious Diseases, 101, 242-251. [Google Scholar] [Crossref]

6. Wangdi, K., Singhasivanon, P., Silawan, T., Lawpoolsri, S., White, N. J., & Kaewkungwal, J. (2020). Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan. Malaria Journal, 19(1), 60. [Google Scholar] [Crossref]

7. World Health Organization. (2023). World Malaria Report 2022. Geneva: WHO. [Google Scholar] [Crossref]

8. Zimbabwe Meteorological Services Department. (2021). Climate Data Report for Matabeleland South Province 2015-2021. [Google Scholar] [Crossref]

9. Zimbabwe Ministry of Health and Child Care. (2022). Annual Malaria Control Program Report 2021-2022. Harare: Government of Zimbabwe. [Google Scholar] [Crossref]

10. Zimbabwe National Malaria Control Program. (2022). Malaria Strategic Plan 2021-2025. Harare: Ministry of Health and Child Care. [Google Scholar] [Crossref]

11. Zimbabwe National Statistics Agency. (2022). Population Projections Thematic Report. Harare: ZIMSTAT. [Google Scholar] [Crossref]

• Tribal Child Nutrition and Health in District of Sundargarh: A Public Health Review of ICDS Intervention
• Knowledge, Attitudes and Practices Towards Prostate Cancer Screening Amongst Men Aged 40-60 Years in The Buea Health District: A Cross-Sectional Study
• Compliance with JCI Protocols: A Focus on Employee Safety
• Influence and Involvement of Teachers in Menstrual Hygiene Management of Female Secondary School Students in Kogi State, Nigeria
• A Critical Evaluation of Ayushman Bharat Pradhan Mantri Jan Arogya Yojana in Bihar