## 2 Related Work
### Deep Learning and COVID-19 Forecasting  
Several studies focused on forecasting coronavirus transmission in various parts of the world were interested in analyzing the states of this virus's spread. Here we will discuss the most critical publications at present and their research contributions. A mathematical model was implemented by [PERSON] et al., which uses serial Monte Carlo simulations where the rate of early virus transmission is determined by calculating the average number of daily reproduction, or the time-varying basic reproduction number called Rt in various measures, such as the proportion of confirmed cases. Suppose the communication becomes homogeneous, and the risk of spreading the disease increases [5]. In addition, the transmission of the virus was controlled by deriving a mathematical model that relies on isolation and contact tracing. The delay in the back of symptoms is determined, and isolation leads to an increase in the probability of spreading the virus. There is doubt about identifying symptoms if the stage is early and a lower evaluation test threshold, which lengthens the wait in detecting signs. It is expected that there will be an effect on more people [6]. It is also possible to take advantage of modern technologies to obtain various predictions for different aspects of the answer to COVID-19 using AI, machine learning, deep learning, and massive data. Below are some of the critical ways technologies can be used, such as early diagnosis of many diseases, contact tracing, development of drugs and vaccines, making predictions of future cases, etc. [7]. Another study used a clustering algorithm known as K-means; this is an unsupervised machine learning algorithm. The aim is to gather COVID-19 data for various predictive variables and concepts. The model assisted in studying the state of countries infected by coronavirus or those that are very likely to be affected by it shortly [8]. Additionally, some recent works aimed at predicting cases of coronavirus are illustrated. For instance, in 2020, [PERSON] et al. developed a CNN system to discover coronavirus patients. This was done by employing a computerized tomography (CT) scan that scans the chest. Better results were obtained using the CNN model in that detection of COVID-19 via a CT scan is performed to examine the chest by comparing the conventional RT-PCR test. Overall, the CNN model is a COVID-19 infection detection model that is both accurate and fast [9]. In addition, [PERSON] et al., in their study, used one of the deep learning algorithms for chest tomography of patients who were infected with coronavirus to determine the impact on the lungs of those infected, where the coronavirus patients were classified as moderate; based on CT scan findings, cases are categorized as mild, serious, or critical. Using a chest X-ray as well as a health assessment and lab findings, a deep learning algorithm was applied to determine the extent of the patient's lung disease. The researchers found that lung opacity assessment using a chest CT scan differed greatly from the assessment of clinical groups, insisting that the model can eliminate the subjectivity that resulted from the initial evaluation in the original estimation of the coronavirus results [10]. On the other hand, [PERSON] et al. developed a joint-based model, which includes a CNN in addition to a random tree, a support vector machine (SVM), random forest, and the result of a CT scan of the chest and non-imaging clinical information with the aim of early prediction of the coronavirus infection. The CNN is applied to a tomography image. Other algorithms for coronavirus have been classified using clinical information that is invisible and using a set of outputs from the CNN algorithm and various other algorithms to predict the patient's infection with the virus. Using these three models, the diagnostic tool can quickly detect patients with coronavirus [11]. [PERSON] et al. diagnosed coronavirus in patients by developing a generative adversarial network (GAN) and a CNN variant.