TITLE:
Morbidity-Based Forecasting Improves Accuracy of Malaria Commodity Quantification in Zambia: A Comparative Analysis of Routine Program Data
AUTHORS:
Lahaye Malembeka Kapobe, Webrod Mufwambi, Siphiwe Makowane, Racheal Samudata, Chipasha Mbuzi, Vianney Neene, Steward Mudenda
KEYWORDS:
Malaria, Forecasting, Morbidity-Based Quantification, Supply Chain, Antimalarial Commodities, Artemether-Lumefantrine, Rapid Diagnostic Tests, Zambia
JOURNAL NAME:
Open Journal of Business and Management,
Vol.14 No.4,
July
1,
2026
ABSTRACT: Background: Accurate forecasting of malaria commodities is essential to ensure an uninterrupted supply and effective disease control in endemic settings such in Zambia. In many low- and middle-income countries, consumption-based forecasting remains the standard approach, despite its inability to capture seasonality, trends and fluctuations. This study compared the accuracy of morbidity-based and consumption-based forecasting approaches for malaria commodities in Zambia. Methods: A comparative retrospective cross-sectional study was conducted using longitudinal national data from 2022 to 2025. Malaria morbidity data from the health management Information system (HMIS) and consumption records from the electronic logistics management information system (eLMIS) for key malaria commodities, artemether-lumefantrine, and malaria rapid diagnostic tests (mRDTs) were extracted and analysed. National forecast figures for 2022 to 2025 for morbidity and consumption-based methods were evaluated using mean absolute error (MAE) and the mean error (ME). Statistical significance testing was performed using the Wilcoxon signed rank tests and Diebold-Mariano tests to ascertain the statistical significance of the forecast performance of the morbidity forecast method against the consumption forecast method. Results: Morbidity-based forecasting consistently outperformed consumption-based methods for all commodities. The morbidity-based approach achieved substantial reductions in forecast error, with improvements ranging from approximately 35.95% to 75.85%. These differences were statistically significant (p Conclusion: Morbidity-based forecasting provides a more accurate and epidemiologically aligned approach to malaria commodity forecasting than consumption-based methods in Zambia. Transitioning to morbidity-driven forecasting could improve supply chain efficiency, reduce stockouts, and enhance malaria treatment outcomes. Future research should focus on integrating environmental and seasonal variables and developing hybrid forecasting models that further improve predictive accuracy.