### Related Work  
For COVID-19 forecasting, many researchers have developed DL-based approaches for analyzing and predicting the number of infections, recoveries, and deaths in different locations [7; 8; 9; 19; 30; 31]. Because of the high sensitivity of the disease dynamics with location features, the reported results were consequently different from one location to another, even in the same country. In Table 1 below, the case studies selected including the reference, country/location, DL techniques used, period covered, results, advantages, and shortcomings are summarized. \begin{table}
\begin{tabular}{c c c c c c c} \hline \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Country/Location** & **Used Technique** & **Period** & **Results** & **Advantages** & **Shortcomings** \\ \hline
[24] & Saudi Arabia, Brazil, India, South Agria, Spain, USA & & 2 March & LSTM 11\% better & Capability of learning & Hyperparameters determined using nonlinear features of COVID-19 data & a parametric study \\ \hline
[25] & Brazil, Russia, UK & LSTM, GRU, CNN, MCNN & Up to 18 &
\begin{tabular}{c} CNN has \\ outperformed in terms of \\ hyperparameter \\ metrics \\ \end{tabular} & Good & LSTM provided \\ \hline
[26] & India/Chennai & ARIMA, LSTM, SLSTM, Prophet & 22 January 2020–8 May & LSTM 2\% better than other DL algorithms & performances are reported by the authors & Reliability depends on the dataset and location \\ \hline
[27] & India & RNN, Modified LSTM, DRL & 30 January 2020–16 August & MLSTM-DRL was found to outperform the other methods &
\begin{tabular}{c} High \\ predictability and \\ good agreement \\ between \\ predicted and \\ recorded data \\ \end{tabular} & Case-sensitive \\ \hline
[28] & India, Argentina, France, South Korea, Germany, Russia, UK, Italy & Combination of SIRVD and DL algorithms & 15 January 2021–27 May &
\begin{tabular}{c} SIRVD-DL: \(R^{2}\) = \\ 0.995 and MAPE \\ outperformed \\ standalone \\ methods \\ \end{tabular} & Hybrid methods yielded better results than standalone methods & Computationally hard \\ \hline
[29] & Pakistan, Afghanistan, India, Bangladesh & LSTM, RNN, GRU & 22 January 2020–21 June & Accuracy rate greater than 90\% & Ability to forecast the next 10 & No details about hyperparameters’ \\ \hline
[30] & Australia, Iran & LSTM, Conv-LSTM, GRU, Bi-LSTM, Bi-Conv-LSTM, Bi-GRU & 25 January 2020–19 August & \
\begin{tabular}{c} Bidirectional \\ models were \\ found to yield \\ better than their \\ simple form \\ (without \\ backward \\ components) \\ \end{tabular} & Two techniques were reported by the authors to be used for the first &
\begin{tabular}{c} No information \\ about the \\ hyperparameters \\ settings \\ \end{tabular} \\ \hline
[31] & Malaysia, Morocco, Saudi Arabia & RNN, LSTM & 15 March 2020–3 December 2020 & \begin{tabular}{c} 98.58\% of \\ accuracy LSTM \\ and 93.45\% RNN \\ \end{tabular} & \
\begin{tabular}{c} Seven-day-ahead \\ forecasts \\ \end{tabular} &
\begin{tabular}{c} High \\ computational \\ burden \\ \end{tabular} \\ \hline
[10] & Italy & \begin{tabular}{c} SIRCQTHE \\ epidemiological model \\ model \\ \end{tabular} & Italian real data & \begin{tabular}{c} Efficient feedback \\ control laws \\ \end{tabular} & \
\begin{tabular}{c} Benefiting from \\ modeling to \\ design control \\ strategies \\ \end{tabular} &
\begin{tabular}{c} Modeling context \\ the purpose of \\ for control not for \\ prediction) \\ \end{tabular} \\ \hline
[11] & Italy & \begin{tabular}{c} Model predictive \\ optimal control based on SIR model \\ \end{tabular} & Italian multi-region 2020 & \begin{tabular}{c} Synthesis of \\ model predictive \\ regional and \\ national levels \\ \end{tabular} & \
\begin{tabular}{c} SIRQTHE model \\ used for a model \\ predictive \\ optimal control \\ strategy \\ \end{tabular} &
\begin{tabular}{c} The study is \\ relatively old and \\ does not cover a \\ large period of \\ the COVID-19 \\ pandemic \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.