Department of Computer Science, Periyar University, Salem-India.
Afr Health Sci. 2023 Mar;23(1):93-103. doi: 10.4314/ahs.v23i1.11.
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.
The aim of this study is to forecast vaccination rate for COVID-19 in Africa.
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.
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.
HGRNN model provides accurate daily vaccinated case forecast, which helps to maintain optimal vaccine stock to avoid vaccine wastage and save many lives.
公共卫生部门可以使用预测应用程序来确定疫苗库存需求，以避免库存过剩或短缺。这种预测将确保 COVID-19 的免疫保护在非洲公民中得到很好的分配。
本研究旨在预测非洲 COVID-19 的疫苗接种率。
用于估计预测的方法是混合预测模型，用于预测 COVID-19 疫苗接种率（CVR）。HARIMA 是 ARIMA 和线性回归模型的混合体，HGRNN 是广义回归神经网络（GRNN）和高斯过程回归（GPR）模型的混合体，用于提高预测准确性。
在这项研究中，标准和混合预测模型用于评估 2021 年 5 月和 6 月的每日新 COVID-19 疫苗接种情况。为了评估模型的有效性，使用了非洲 COVID-19 疫苗数据集，其中包括 2021 年 1 月 13 日至 2021 年 5 月 16 日的每日新疫苗接种情况。在这个过程中，使用均方根误差（RMSE）和误差百分比（EP）作为评估指标。结果表明，混合 GRNN 模型的性能优于混合 ARIMA 模型。
HGRNN 模型提供了准确的每日接种病例预测，有助于维持最佳疫苗库存，避免疫苗浪费，拯救许多生命。
