Predicting Malaria Trends in Zambezi District of Zambia Using ARIMA Modeling Techniques: A 2019–2024 Time Series Analysis

Malaria has continued to show a major public health concern in some parts of Zambia, where the Zambezi district of North Western province is being particularly affected. The district experiences seasonal outbreaks that strain health care resources and impact community well-being. Despite ongoing efforts in malaria control and prevention, predicting malaria remains complex due to various environmental, social, and economic factors. This study was aimed at examining the recent trends of malaria in Zambezi district and predicting future trends using autoregressive integrated moving average (ARIMA) techniques that will help improve the efficiency of malaria prevention and control interventions in the district. The study adopted a retrospective comparative design by utilizing the routine malaria data from the Zambezi district elimination center for the period from 2019 to 2024. Malaria prevalence trends were examined across both annual and monthly periods. This study used secondary data obtained from monthly malaria case records of health centers in Zambezi district. The SARIMA (2,1,1) (0,1,1)12 model was employed to predict monthly malaria incidence in the district over the 2019 to 2024 period. Malaria incidence indicated a consistent seasonal pattern, with March recording the highest number of cases, while August consistently showed the lowest across the years. From the study period, 2019 emerged as the best-performing year, because it reported the lowest total number of malaria cases (638). The Forecast results indicate that malaria cases during the first half of 2025, corresponding to the rainy season, are projected to reach approximately 583 cases, while the second half of the year, representing the dry season, is expected to record about 354 cases. With the base on these findings, the selected model is recommended for use by the Zambezi district Health Directorate and researchers for monitoring and forecasting malaria cases in Zambezi district and similar/comparable settings. Furthermore, targeted interventions are recommended, particularly during periods associated with higher malaria incidence, to effectively reduce disease burden.