# 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  
Several works that use deep learning in forecasting COVID-19 were found in the literature. The authors [PERSON] et al. [21] proposed an improved susceptible-infected (ISI) model to analyze the development law of COVID-19 in China. Their method used the ratio of the number of newly confirmed cases at a certain time to the cumulative number of new cases over different time scales to calculate the infection rate for their epidemic model. A LSTM network was used to estimate the rate deviation of the epidemic and combine with ISI was used to estimate the number of infected cases. To include the effect of events and government control measures, a pre-trained natural language processing (NLP) model named BERT was used to extract features from relevant news articles of various cities and combined with the LSTM network to correct the deviation of infection rate and further predict a more accurate number of infected cases. The Centre of Disease Control and Prevention (CDC) data was used for this paper. It was found that NLP features provided extra information and guidance for more accurate prediction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Purpose** & **Dataset** & **Methods** & **Best Results** \\ \hline \multirow{4}{*}{[PERSON] et al. [12]} & Predict number of & \multirow{2}{*}{Global totals from \(  
[PERSON] et al. [22] also proposed the use of an LSTM to predict the spread of COVID-19 in New Zealand using data extracted over a period of 11 months from the Worldometer website. The authors aimed to predict when the virus could be contained. To achieve that aim, the authors calculated a range with an error rate of 1% from either side of the date when it was first predicted as zero cases. To validate their model, they utilized the data for New Zealand, which has already controlled the spread of the pandemic. The model predicted that the curve depicting the number of new cases daily would flatten to zero around 25 April 2020 before rising again and eventually flatlining on 15 May 2020, whereas New Zealand got its first zero cases on 13 May although there were one or two more cases a few days afterwards. For countries where the number of new cases was decreasing, the authors used a time step of one day and predicted the number of cases for the next day; where the number of cases was increasing, the authors used a two-phase procedure where in the first phase, the log of the number of daily cases used a seven-point moving-average filter to determine the peak, and after the peak, the same method of the decreasing number of cases was applied. To avoid overfitting and improve generalization, Bayesian optimization was applied to tune the hyper-parameters of the LSTM network. Similarly, [PERSON] et al. [23] used a LSTM network to forecast COVID-19 transmission in Canada. The authors used the data from Johns Hopkins University and the Canadian Health Authorities that included several features, namely the number of confirmed cases to 31 March 2020, the number of deaths, and the number of recoveries, along with the dates. The authors also inversely trained their network and found that the start of the outbreak in Canada was around early January 2020 but was not actually reported until the last week of January. Another useful case is from [PERSON] et al. [24], who also proposed using a LSTM for short-term forecasting of COVID-19 cases in Turkey. The authors used data from the Turkish Ministry of Health that consisted of number of confirmed cases from 11 March 2020 to 8 May 2020. It was also found that using an exponential linear unit for activation function worked better as compared to the common choice of hyperbolic tangent. [PERSON] et al. [25] proposed a deep learning time series model to forecast the range of increase in COVID-19 data in the future days using large numbers of heterogeneous features.