TITLE:
Reducing Forecast Errors in HIV Test Kit Quantification Using Region-Specific Models: A Mixed-Methods Analysis of Routine Data from Zambia
AUTHORS:
Chipasha Mbuzi, Webrod Mufwambi, Siphiwe Makowane, Racheal Samudata, Lahaye Malembeka Kapobe, Vianney Neene, Steward Mudenda
KEYWORDS:
HIV Testing, Test Kit Quantification, Forecasting, Supply Chain Management, Regional Variation, Routine Program Data, Commodity Security, Zambia
JOURNAL NAME:
Open Journal of Business and Management,
Vol.14 No.3,
May
29,
2026
ABSTRACT: Background: Accurate quantification of human immunodeficiency virus (HIV) test kits is essential for ensuring uninterrupted testing services and achieving HIV control targets. In Zambia, quantification is largely conducted using national-level approaches that may not adequately capture regional variations in the demand for testing. This study assessed regional differences in HIV testing patterns and evaluated the need for region-specific approaches to quantify HIV test kits in Zambia. Methods: This convergent parallel mixed-methods study combined longitudinal consumption data from 517 health facilities (2020-2024) with qualitative insights from eight key informants. A retrospective analysis was conducted using routine program data on HIV testing and test kit consumption across three Zambian provinces. A regionalised exponential smoothing with trend and seasonality (REST-S) model was developed with province-specific parameters for distinct epidemiological and operational contexts. Data from 2020 to 2024 were used to assess the testing volume, positivity rates, and consumption patterns. Comparative analyses across regions evaluated discrepancies between estimated and actual consumption to identify inefficiencies in the quantification approaches. The forecast accuracy was assessed using the mean absolute percentage error (MAPE). Qualitative themes were numerically coded and correlated with quantitative performance using Spearman’s rank correlation. Results: Substantial regional variations in HIV testing demand and test kit consumption were observed across provinces. High-burden regions demonstrated consistently higher testing volumes and positivity rates, whereas low-burden regions exhibited fluctuating demand patterns. Provincial consumption patterns diverged substantially from the national forecasts. Lusaka consumed 44% more than allocations (95% CI: +38.2% to +49.8%), while Copperbelt exhibited the highest volatility (coefficient of variation = 42.3%, CAGR 22.2%). The current national forecasting model produced large forecasting errors across all provinces (weighted average MAPE 28.7%, 95% CI: 25.1% - 32.5%), exceeding the WHO error thresholds. The REST-S model achieved a 62% - 76% error reduction, with the MAPE declining to 6.8% - 12.5% across provinces. Diebold-Mariano tests confirmed superior predictive accuracy (p p = 0.015) between quantitative forecast errors and qualitative assessments of operational challenges validated the model performance. The use of uniform national forecasting approaches resulted in overestimation in some provinces and underestimation in others, contributing to stock imbalances, including both stockouts and overstocking. These inefficiencies highlight the limitations of the current centralised forecasting model. Conclusion: Region-specific forecasting approaches to HIV test kit quantification can improve forecasting accuracy and enhance supply chain efficiency in Zambia. Tailoring forecasting models to regional epidemiological and service delivery patterns could reduce stock imbalances, save limited resources, and support more effective HIV testing programs. Further research should explore the integration of epidemiological, programmatic, and demographic factors into adaptive forecasting models.