Vaccine rate forecast for COVID-19 in Africa using hybrid forecasting models

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 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 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 are used as evaluation measures in this process. The results obtained showed that the hybrid GRNN model performed better Conclusion: HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives. Keywords: Vaccination forecasting; ARIMA; Immunization; Time series techniques; Hybrid ARIMA; Prediction; Linear Regression;