Table 1.

List of all the summarized works

Ref.	Proposed Technique	Contributions	Research Gap
Kim [22]	Statistical models and deep learning (DL) model (LSTM-DNN)	LSTM-DNN achieved better performance when compared to ARIMA and GARCH’s root mean squared error.	Data collected after February 2021 was not included in the analysis. Also, the accuracy of the proposed model is quite poor.
Cheong et al. [23]	Machine Learning	Discovered the most important socioeconomic factors in predicting vaccination uptake in US.	The dataset employed in the work is limited to the United States, and the prediction accuracy is relatively poor.
Abdulkareem et al. [24]	Decision Tree (DT), K-nearest neighbors (KNN), Random Tree (RT), and Naive Bayes (NB)	In terms of time and accuracy, this was an excellent performance.	The results of the ML models employed in the experiment were not compared to those of high-performing machine learning algorithms utilizing the same dataset. Poor performance of many of the ML models used except for decision tree.
Fernandes et al. [25]	Machine Learning	ANN model achieved vaccination intention prediction accuracy of 85%.	Prediction accuracy is low. Also, the work did not cover data obtained after 14 March 2021.
Zaidi et al. [26]	Machine Learning	Overall accuracy of 89.9% for SVM.	Total prediction accuracy is low.
Davahli et al. [27]	Deterministic and stochastic LSTM and MDN	Alternative prediction techniques were surpassed by a deterministic LSTM model trained on the COVID-19 optimal reproduction numbers.	The dataset that was used was only for the United States. The influence of states on one another was not considered by the authors. Dataset used for the work is limited to just three months data (August 26, 2020 to November 26, 2020).