• Claris Shoko, Caston Sigauke, and Peter Njuho.
• Department of Mathematics and Computer Sciences, Great Zimbabwe University. Private Bag 1235, Masvingo.
• Afr Health Sci. 2022 Dec 1; 22 (4): 534550534-550.

BackgroundThe coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem.ObjectiveTo formulate an effective model that timely predicts the spread of COVID-19 in the SADC region.MethodsUsing the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM).ResultsAmong the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278).ConclusionThe findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.© 2022 Shoko C et al.

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