LAUSR

Background: The public health sectors can use the forecasting applications to determine vaccine stock requirements to avoid excess or shortage stock. This prediction will ensure that immunization protection for COVID- 19 is well-distributed among African citizens. Objective: The aim of this study is to forecast vaccination rate for COVID-19 in Africa Methods: The method used to estimate predictions is the hybrid forecasting models which predicts the COVID-19 vaccination rate (CVR). HARIMA is a hybrid of ARIMA and the Linear Regression model and HGRNN is a hybrid of Generalized Regression Neural Network (GRNN) and the Gaussian Process Regression (GPR) model which are used to improve predictive accuracy. Results: In this study, standard and hybrid forecasting models are used to evaluate new COVID-19 vaccine cases daily in May and June 2021. To evaluate the effectiveness of the models, the COVID-19 vaccine dataset for Africa was used, which included new vaccine cases daily from 13 January 2021 to 16 May 2021. Root Mean Squared Error (RMSE) and Error Percentage (EP) are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better than the hybrid ARIMA model. Conclusion: HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock toavoid vaccine wastage and save many lives. Keywords: Vaccination forecasting; ARIMA; Immunization; Time series techniques; Hybrid ARIMA; Prediction; Linear Regression; Gaussian Regression Process; Hybrid GRNN.