# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Exponential Smoothing**} & \multicolumn{1}{c}{**LASSO**} & \multicolumn{1}{c}{**LSTM**} & \multicolumn{1}{c}{**LSTM-AE**} & \multicolumn{1}{c}{\begin{tabular}{c} **Bi-Directional LSTM** \\ \end{tabular} } \\ \hline \multirow{4}{*}{\begin{tabular}{c} Univariate \\ (Confirmed Cases \\ as input) \\ \end{tabular} } & Train & \(R^{2}\) & - & - & \(9.10\times 10^{-1}\) & \(5.00\times 10^{-2}\) & \(7.10\times 10^{-1}\) \\ \cline{2-8}  & RMSE & - & - & \(2.68\times 10^{2}\) & \(8.82\times 10^{2}\) & \(4.84\times 10^{2}\) \\ \cline{2-8}  & & \(R^{2}\) & \(7.00\times 10^{-2}\) & - & \(9.10\times 10^{-1}\) & \(4.50\times 10^{-1}\) & \(8.60\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(2.76\times 10^{2}\) & \(6.80\times 10^{2}\) & \(3.33\times 10^{2}\) \\ \cline{2-8}  & & \(R^{2}\) & - & - & \(9.10\times 10^{-1}\) & \(2.00\times 10^{-2}\) & \(6.50\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(2.65\times 10^{2}\) & \(9.00\times 10^{2}\) & \(5.31\times 10^{2}\) \\ \cline{2-8}  & & \(R^{2}\) & - & \(9.80\times 10^{-1}\) & \(8.80\times 10^{-1}\) & \(3.50\times 10^{-1}\) & \(5.70\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(3.15\times 10^{2}\) & \(7.40\times 10^{2}\) & \(5.99\times 10^{2}\) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Multivariate \\ (Added Recovered \\ Cases and Death \\ to input) \\ \end{tabular} } & Train & \(R^{2}\) & - & - & \(9.10\times 10^{-1}\) & \(3.00\times 10^{-2}\) & \(6.80\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(2.61\times 10^{2}\) & \(8.92\times 10^{2}\) & \(5.11\times 10^{2}\) \\ \cline{2-8}  & & \(R^{2}\) & - & \(9.80\times 10^{-1}\) & \(8.60\times 10^{-1}\) & \(6.00\times 10^{-2}\) & \(1.73\times 10^{0}\) \\ \cline{2-8}  & & RMSE & - & - & \(3.43\times 10^{2}\) & \(9.44\times 10^{2}\) & \(1.52\times 10^{3}\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Multivariate \\ (Added Events to \\ the input) \\ \end{tabular} } & Train & \(R^{2}\) & - & - & \(9.10\times 10^{-1}\) & \(2.00\times 10^{-2}\) & \(7.10\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(2.70\times 10^{2}\) & \(9.01\times 10^{2}\) & \(4.89\times 10^{2}\) \\ \cline{2-8}  & & \(R^{2}\) & - & \(9.80\times 10^{-1}\) & \(8.50\times 10^{-1}\) & \(1.73\times 10^{0}\) & \(6.10\times 10^{-1}\) \\ \cline{2-8}  & & RMSE & - & - & \(3.47\times 10^{2}\) & \(1.52\times 10^{3}\) & \(5.71\times 10^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of all the experimentation conducted. Figure 9: Prediction using the LASSO Regression model on the testing set. Day 1 has a better score, with just one day of history, it can easily overfit the model and, hence, the next best value of a lag of five days was selected. We can already see a huge improvement on the model using the univariate trend as compared to [28]. This is illustrated in Figure 10. We can also see that the prediction line has started to not flatline and to forecast some values. This shows that the original model was not trained with enough data. Although this model is not perfect either, we can see that more data do help improve it drastically, and in the future, the model may be perfected. This also proves that LSTMs highly depend on trends and seasonality and enough of these factors should be captured in the training set. The following Figure 11 illustrates the way the LSTM worked on the other variations of the dataset. From this, we could infer that the model works moderately well with the addition of the Confirmed Cases, Deaths, and Recoveries attributes, except for a few areas marked on the Figure where the model gets the values slightly wrong (b), although one would think that the addition of the Recovered Cases and Deaths attributes should help improve the model as they correlated to each other, as seen in Figure 3. This could be because in the UAE dataset, deaths are almost constant, and this might cause a confusion to the trend.