. 2021 Dec 14;141:105141. doi: 10.1016/j.compbiomed.2021.105141

Table 7.

The methods, properties, and features of RNN-COVID-19 mechanisms.

Authors	Main idea	Advantages	Research challenges	Security mechanism?	Dataset	Using TL?	Method	Usage
Alakus and Turkoglu [67]	Normalizing and classifying the mapped protein sequences using the DeepBiRNN model.	−97.76% accuracy, 97.60% precision, 98.33% recall, 79.42% f1-score, and an overall AUC of 89%.	-There has been no comparison with the modern approaches.	No	NCBI dataset	No	RNN + AVL tree	Protein interactions in COVID-19 disease prediction
Alakus and Turkoglu [67]		-Stable and robust	-There has been no comparison with the modern approaches.	No	NCBI dataset	No	RNN + AVL tree	Protein interactions in COVID-19 disease prediction
ArunKumar, Kalaga [68]	Using RNN and GRUs, predict future patterns in cumulative reported cases, cumulative recovered cases, and cumulative fatalities in the top 10 countries.	-High accuracy	-High energy consumption	No	Datasets from John Hopkin's university are publicly accessible	No	RNN + GRU	Forecasting cumulative reported cases, cumulative recovered cases, and cumulative fatalities by region
ArunKumar, Kalaga [68]		-Great analysis of results.	-High delay	No		No	RNN + GRU
Kumari and Sood [69]	Using RNN to construct a prediction model.	-A 98% overall accuracy	-There has been no comparison with the modern approaches.	No	Kaggle dataset	No	Simple RNN	Forecasting future, fatalities, and recovered cases patterns
Kumari and Sood [69]	Using RNN to construct a prediction model.	-Low system complexity	-There has been no comparison with the modern approaches.	No	Kaggle dataset	No	Simple RNN
Li, Jia [70]	Using attention-based RNN architecture to predict the epidemic trends for different countries.	-High accuracy	-High delay	No	The dataset includes daily, the lockdown history, and populations from 83	Yes	Attention-based RNN	Forecast epidemic patterns in various countries
		-Useful tool for predicting the need for a county-wide lockdown.	-The number of cases recovered, deaths, and available healthcare services are not taken into account.
		-High scalability
		-Large dataset used.
Shastri, Singh [71]	Using RNN-based LSTM variants, propose a technique for forecasting Covid-19 cases for one month ahead.	-High accuracy	-High complexity	No	Dataset from the India and USA Department of Health.	No	RNN	Covid-19 forecasting for India and USA
		-High predictability	-High delay
			-High energy consumption