# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 5 Discussion  
As shown in the previous section, multiple models with various combinations of attributes were studied to future forecast COVID-19 in the UAE. Table 8 summarizes the results of the models developed in this paper. It can be seen that four of the models have competitively outperformed the original UAE paper [28], with an improvement of over 30%. A more recent work published in 2024 [43] also tests multiple deep learning models for forecasting COVID-19 in the UAE and Malaysia. The authors reported an MAE of 0.046 and an \(R^{2}\) score of 0.004 for their Univariate LSTM model. Our proposed model achieved an \(R^{2}\) score of 0.91, which shows that the model learned the behavior better. Figure 12: **(a)** Prediction using the bidirectional LSTM-AE on the training set using multivariate data (**b**) Prediction using the bidirectional LSTM-AE on the testing set using multivariate data (**c**) Prediction using the bidirectional LSTM-AE on the training set using vaccination data (**d**) Prediction using the bidirectional LSTM-AE on the testing set using vaccination data (**e**) Prediction using the bidirectional LSTM-AE on the training set using event data (**f**) Prediction using the bidirectional LSTM-AE on the testing using event data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Data Size** & **Model** & **RMSE** \\ \hline Proposed work & 791 & Univariate LSTM & \(2.76\times 10^{2}\) \\ Proposed work & 791 & Multivariate LSTM & \(3.15\times 10^{2}\) \\ Proposed work & 791 & Multivariate LSTM + Vaccinations & \(3.43\times 10^{2}\) \\ Proposed work & 791 & Multivariate LSTM + Events & \(3.47\times 10^{2}\) \\ [PERSON] et al. [28] & 333 & Univariate LSTM & \(4.46\times 10^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of recent works for forecasting COVID-19 cases in UAE. The models developed in this work were compared to other UAE works only due to the fact that different countries were affected by different magnitudes of COVID-19 outbreak, as can be noticed from the literature review. Therefore, it is not fair to compare the results of this work to the works of other countries. Furthermore, the proposed work is the only one that used vaccination data as an attribute. From these results, we can infer that LSTM works better with more data. It can also be gleaned that, due to the simplicity of the nature of the data, a relatively simple LSTM architecture is sufficient to forecast the number of cases. From prior knowledge, one would think that vaccinations, policies, and events would play a part in the spread of COVID-19. This can be seen partially here, with the improvement of predictions when those attributes were added, but the performance is still not the best due to the limited information provided to the model due to the nature of the data. It is suspected that, after the initial drives and efforts to curb the rapid contagion of COVID-19, public health measures and other similar policies remained roughly the same for many months during these periods without major changes. Thus, it could be said that concordance with public health measures such as mass vaccinations, booster drives, and quarantines helped flatten the curve only during the most infectious incubation period. Another factor is that the breakdown of cases by COVID-19 variant (original, Omicron, Delta, etc.) is not available, which means there is no \"impurity\" in the data, and no additional information that can be leveraged. This falls under the virus characteristics, where the mutation rate changes based on the different variants of the virus that are simultaneously rampant among the population. Since the active cases, the rate of infection, and even the mortality rate were different across different strata of the population (labor camps, social workers, essential staff, students, medical professionals, etc. ), it would have been useful to have the average susceptibility of each demographic per day or per month to offer more modelling power.