Machine-learning forecasting model of tuberculosis cases among children in South Africa

Globally, children and young adolescents under 15 years old constitute approximately 11% of all tuberculosis (TB) cases, with a growing concern over TB infections in children under 5 years old, especially in resource-limited settings. Nonetheless, the true extent of TB burden among children remains inadequately explored in South Africa. The application of a random forest–Bayesian autoregressive integrated moving average (RF-BARIMA) model for infectious disease prediction has not been previously employed to study TB in children. In this study, we employed the RF-BARIMA model to forecast TB incidences, from 2010 to 2019, among children under 5 years old in South Africa’s Eastern Cape Province. Comparative analysis demonstrated that the RF-BARIMA model outperformed other models in predictive accuracy and forecast capability. Our predictions revealed a projected mean of 0.4122 TB cases per month in 2022, with an effective sample size of 4054 TB cases in the Eastern Cape Province. These findings indicate a prospective reduction of 1670.85 TB cases among children under 5 years old in the coming years. The RF-BARIMA model offers enhanced predictive and forecast accuracy in comparison to the single Bayesian ARIMA model. These results provide compelling evidence of significant under-reporting and potentially elevated TB incidence among children under 5 years old in South Africa’s Eastern Cape Province, raising important implications for public health policy and intervention strategies.