Machine Learning Algorithms for the Prediction of the Spread of COVID-19 in Namibia

Improving the accuracy and stability of daily COVID-19 forecasts is crucial for effectively managing and controlling the pandemic. This chapter compares the performance of different machine-learning algorithms in predicting and forecasting the spread of COVID-19 in Namibia. Machine learning approaches that include the support vector machine (SVM), the TBATS, the generalized additive model (GAM), and the Stochastic Gradient Boosting Machine (SGBM) approach are compared. Selection of the best-performing model is done using plots of forecasts from fitted models on the test dataset since plots are visually appealing. A further selection of the best model is done using key performance indicators (KPIs), that is, root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination R2. Results show that the positive rate, reproductive rate, and stringency index contribute significantly (p-values