## 2 Related Work
### Deep Learning and COVID-19 Forecasting
The dataset used in the study on coronavirus cases is with the participation of Johns Hopkins University in the United States and the Canadian Health Authority. The obtained MAPE had a 92% accuracy. This high accuracy achieved by the LSTM algorithm proves its effectiveness in dealing with this problem [18]. In Saudi Arabia, [PERSON] et al. forecasted the daily increase in the confirmed cases of COVID-19, one month ahead of time. They used two popular algorithms, ARIMA and ARMA, where ARMA is used as a statistical analysis model. The datasets of COVID-19 cases were obtained from the Saudi Arabian government website. The result found that ARIMA outperformed ARMA [19]. However, in this study, no deep learning algorithms were used, and the time horizon is long and did not consider short-term intervals. Also, this study has not considered forecasting the recovered and death cases in the country. On the other hand, the linear regression model was used by [PERSON] et al. to predict the next eight days of confirmed cases in Nigeria, where the data were collected through the NCDC website. A confidence interval of 95% accuracy was achieved as a result [20]. Additionally, in India, [PERSON] and [PERSON] performed a prediction mission by adopting the LSTM deep learning algorithm to forecast the next month of both the confirmed and recovered case numbers, where the datasets of the study were collected from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The LSTM had a 90% accuracy rate in predicting COVID-19 cases, according to the findings [21]. Likewise, [PERSON] used only the RNN deep learning algorithm to predict the next 30 and 40 days of Brazil's confirmed cases of coronavirus. The data were also collected through the CSSE at Johns Hopkins University. The RNN achieved a forecasting accuracy of 60.17% [22]. Different algorithms were utilized by [PERSON] et al. to predict the next week of confirmed dead and recovered cases in 10 countries. The algorithms used include ARIMA, TBAT, Prophet, and DeepAR, and their proposed model is the Holt-Winters additive model (HWAAS). The datasets used for that were collected mainly from the website of kaggle.com. The findings show that ARIMA and TBAT are better than the others in predicting virus cases [23]. [PERSON] et al. used an artificial neural network, the multilayer perceptron, to forecast the next month of the number of all confirmed, dead, and recovered cases globally. The study's datasets were obtained from CSSE for cases that had been confirmed [24]. Moreover, the algorithms of RNN, LSTM, and VAE were utilized by [PERSON] et al. to predict the next 17 days of confirmed, recovered cases for 6 states. The datasets for all six stateswere also collected from CSSE. Based on the error measures, the results found that variational autoencoder (VAE) forecasting capability is better than the other algorithms [25]. [PERSON] et al. used four algorithms to forecast the next day or week of India's confirmed cases. The information and data were gathered from the Ministry of Health and Family Welfare. The analysis showed that the bidirectional LSTM gives better results and minor errors [26]. [PERSON] et al. suggested a deep learning transfer strategy to predict coronavirus patients automatically in which chest X-ray images were taken from coronavirus patients. A healthy person is used as data for the model. This data include 50 patients infected with coronavirus. From the shared GitHub repository, X-rays were taken. In addition, 50 X-rays of recovered and healthy people were taken from a Kaggle warehouse. According to the results, the model that was learned in advance and the residual network, which is denoted by ResNet50, have given a high prediction accuracy of 98%. This ratio is higher than the other models [27].