# 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 Machine Learning  
Several studies in the literature utilized machine learning techniques to forecast COVID-19 trends. [PERSON] et al. [12] compared four ML models for forecasting of COVID-19. The authors used these models to predict three numerical outputs, namely the number of newly infected cases, the number of deaths, and the number of recoveries in the next 10 days. The authors used the Johns Hopkins University dataset. The four ML algorithms used were Linear Regression, LASSO Regression, Support Vector Machine (SVM), and Exponential Smoothing. It was found that Exponential Smoothing performed the best given that the size of the dataset was very small. LASSO worked well only for predicting death rates and confirmed cases. SVM proved to perform the worst of the four techniques.  
Similarly, the authors [PERSON] et al. [13] compared six ML models for forecasting COVID-19 in India. The authors selected the following features for their dataset: number of cases, announced date of detection, age, detected city, detected district, regional state of detection, gender, and current status. The models, Random Forest (RF), Decision Tree Regression, and Polynomial Linear Regression (LR), were tested. The best performing models were found to be Polynomial LASSO and Polynomial LR.  
[PERSON] et al. [14] selected three features from [15], which are observation date, time, and state/union territory for forecasting. They dropped two features, which were confirmed Indian national and confirmed foreign nations, as these only mattered in the beginning of the pandemic when people were travelling from abroad.  
The authors [PERSON] et al. [16] performed a comparison of Naive Bayes method, Logistic Regression, and K-Nearest Neighbors (KNN) model to predict the recovery of COVID-19 in Indonesia. The authors also proposed future work by adding more variables such as travel history and diet to their data for more accurate prediction.  
[PERSON] et al. [17] used Multiple Regression Analysis to predict the number of deaths due to COVID-19 in India. The authors used a dataset from [18] that consisted of state wise number of cases recorded weekly. The input features for their model were the active cases and the recovered cases. Decision Tree and Auto Regression were used. Their results were in agreement with the ground truth.  
Another use case of utilizing an exponential smoothing is of [PERSON] et al. [19]. The authors used the Johns Hopkins University dataset to perform a 10-step prediction using a non-seasonal Multiplicative Error and Multiplicative Trend Exponential Smoothing model (ETS(MMN)). It was observed that a large forecast error was found in certain areas due to the global measures taken to control the spread and declines in certain countries, which the model was unable to pick up due to the nature of the data. The signed error does show whether a certain policy worked or not in the past. The authors also conducted experiments country wise and found that they had higher percentage errors.  
The authors [PERSON] et al. [20] also evaluated multiple machine learning models to find the best model for predicting COVID-19 infections and deaths in Bangladesh. The authors compared Linear Regression, Polynomial Regression (PR), Support Vector Regression, Auto Regressive Model, Moving Average Model, [PERSON]'s Winter Additive model, Auto Regressive Integrated Moving Average Model, and Facebook Prophet's Model (FP).  
Table 1 provides a summary of some of the studies that used classical machine learning approaches to predict COVID-19 and gives details on the best results.