Table 6.

Techniques, attributes, and characteristics of forecasting-COVID-19 applications

Authors	The basic objective	Pros	Limitations in study	Security method?	Simulation environments	Dataset and Size of Dataset	Using TL?	Mechanism	Application?
Zhan, Zheng [98]	Proposing an ML model for COVID-19 prediction based on a large learning system	-High predictability -High accuracy	-	No	Not mentioned	Dataset from reports made available by national health authorities and the Bureau of statistics (Large dataset)	No	The RF-bagging broad learning system	Prediction
Zhan, Zheng [99]	Proposing a simulated annealing method that is pseudocoevolutionary	-High robustness -High predictability	-Moderate complexity	No	Not mentioned	Real-world records (Large dataset)	No	Simulated annealing	Prediction
Absar, Uddin [100]	Using LSTM to Predict the spread of the epidemic	-High accuracy	-Low flexibility	No	Python- Keras	eHealth division of the government of the Republic of Bangladesh dataset (Small dataset)	No	LSTM	Forecasting pandemic cases
Chiu, Hwang [101]	Determining the axial dependence of the slices using LSTM	-High sensitivity	-Low scalability -A single dataset was used to obtain experimental data	No	Python	NHIRD database (Small dataset)	No	Decision Tree and DNN	Assess the probability of serious disease or death in hospitalized patients
Asgharnezhad, Shamsi [102]	Using CXR images to apply three uncertainty quantification approaches	-High sensitivity and specificity	-Low robustness	No	Python	CXR image database (Small dataset)	No	Ensemble Bayesian networks	COVID-19 detection uncertainty predictions