# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 4 Results  
First, to understand the data and the nature of the series, the LSTM model from the work by [28] was recreated, since the authors used the same dataset. It is also known a priori that all the countries were affected at different levels at different times with the virus, and hence would have different training methods and results. At the time of publishing, the authors were limited to barely one year of data, from 14 January 2020 to 9 February 2021. To keep all the characteristics the same and enable the same grounds of comparison, the data was limited to this time period. Following the paper, a single attribute Confirmed Cases was used, keeping the consistent lag period of seven, after which the value of the same variable is forecasted/predicted. This baseline is initially considered for this period only, with the primary intention of ensuring that the findings are indeed similar. The subsequent experiments extend this baseline by means of additional predictor variables (the earlier works uses only Confirmed Cases), in addition to the consideration of a much longer time frame, i.e., 791 days (ours) versus 333 (theirs). Table 5 reports the results, followed by Figure 7, which shows a visual representation of the testing. Regarding the terminology in Figure 8, it is worth mentioning that we do not use the expressions \"predicted\" and \"forecast\" interchangeably, despite their very similar semantic connotations. The Keyword \"predicted\" is used for new number of Confirmed Cases predicted for windows of time from the testing set while the expression \"forecast\" is used for new number of Confirmed Cases using the \"predicted\" number of Confirmed Cases. Observing further, it appears that the model tends to systematically underestimate the actual values, and a potential reason may be the confounding effect of trend, i.e., the average level of a series. This trend is likely being caused by the presence of a variable that we have not factored in or unknown to us, such as public policy, or the most obvious reason: insufficiency of data. With the relatively high parameterization of deep learning networks with low amounts of data, the true patterns are not detected effectively. As seen in (c), the model forecasts a decline in COVID-19 cases and then starts to flatline and continue at this single value (500). This green line is produced using only newly forecasted data, in simpler terms, it is data that the model has never seen before. Hence, we can say that the model is unable to account for any patterns of seasonality and hence converges the values \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & [28] & **Training** & **Testing** \\ \hline RMSE & \(4.46\times 10^{2}\) & \(1.50\times 10^{2}\) & \(4.95\times 10^{2}\) \\ MRAE & \(5.85\times 10^{1}\) & \(12.39\times 10^{7}\) & \(1.38\times 10^{1}\) \\ MSE & Not reported & \(22.57\times 10^{3}\) & \(24.47\times 10^{4}\) \\ \(R^{2}\) & Not reported & \(7.80\times 10^{-1}\) & \(6.60\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results for the Baseline Model. Figure 7: (a) Prediction using the original LSTM on the testing set (b) Predicting beyond the test set using the original LSTM. and predicts a flatline value, which is just the previous value as it stops considering all the values before the immediately last time step due to insufficient data. Here, seasonality refers to the periodic (cyclical) patterns that occur in the curve as described in Section 1 previously. This \"last \" time-step, somewhere \(\sim\)110 th day outputs a prediction of 500, and this is propagated to the next step, and so on such that only 500 is predicted by the collapsed learning behavior of the model. Table 6 shows the extensive results of the experiments conducted with various combinations of attributes. The combinations are mainly grouped into four cases, them being as follows:  
* Case 1: The input is the previous history of Confirmed Cases while the output is the prediction of the number of Confirmed Cases for the next day.