# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 2 Literature Review
### Forecasting of COVID-19 Cases Using Deep Learning
The features were grouped into three categories, those being constant feature groups, time-dependent feature groups and cross-county time-dependent feature groups. Each of these feature's groups were then embedded so that it can be fed as input the deep learning model. The authors used the DeepFM model with slight modifications. Their model deemed to be effective in forecasting. It was hard to do a comparison with other models due to the nature of their data. The authors also conducted feature importance evaluation and feature interactions to get a clearer idea on what can be used to make COVID-19 related policies. The authors [PERSON] et al. [26] compared five different models to find the most optimum model for forecasting. The authors first trained their model using countrywide data from Johns Hopkins hospital and then applied the best model to Iran. The models were evaluated based on the recovered cases as an output. The five models, Random Forest (RF), Multilayer Perception (MLP), LSTM, LSTM with regular features, LSTM with extended features, and multivariate LSTM (M-LSTM) were judged and evaluated based on MAPE, RMSE, NRMSE, and \(R^{2}\) metrics. The input to the models was the number of confirmed, deaths and recovered cases. The authors also varied the lag to find the most optimal lag parameter, which was found to be six days. To consider the effect of actions taken by the government, the authors stopped the training in three different situations; for example, they stopped the training for dates between 27 March and 4 April 2020, due to road closures between cities. This was to demonstrate what would have happened if the above decisions were not taken by the government. The authors [PERSON] et al. [27] proposed the use of a hybrid CNN-LSTM model to forecast the COVID-19 pandemic. The thought was to combine the advantages of Convolutional Neural Network (CNN) in filtering out noise from the input data and learning the time series representation using the LSTM model which is effective in identifying and modeling short- and long-term temporal dependencies embedded within a data sequence. The authors used the WHO COVID-19 dashboard data that contained countrywide information such as number of confirmed dead, cumulative confirmed, and cumulative deaths. Their hybrid model was compared against 17 other baseline models, which were divided into two deep learning models, two statistical models, three linear models, five ensemble models, and five machine learning models. [PERSON] et al. [28] used a LSTM to predict COVID-19 in the Gulf Cooperation Council (GCC) countries, which includes the UAE. The data were downloaded from the Johns Hopkins dataset for seven months. The inputs were sampled into one-week windows for a seven-time step input and a one-time step output. The data were then fed into a LSTM to generate the output. From their experimentation, they were able to forecast that the number of deaths in the UAE would be under control from the second half of March 2021. For the UAE, the LSTM had a MARE for confirmed cases of 58.50 and a MARE for death cases of 0.63. Table 2 summarizes the studies that used the deep learning approaches to predict COVID-19 and gives details on the best results. From the literature review, it has been found that LSTM is the most common deep learning model that is used for forecasting of COVID-19 cases and that it performs better than the other machine learning models. Amongst the machine learning models, LASSO Regression and Exponential Smoothing seem to have performed the best. Most of the papers have extracted data for a specific country from the Johns Hopkins database [29],