# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 4 Results
### Future Prediction  
Our testing shows that the model is somewhat reasonable. Therefore, we can move on to predicting the future or forecasting. Feeding the past n days' values into the model to predict tomorrow's value, we obtain tomorrow's value as output. To find the day after tomorrow's value, we feed in past n-1 days' values along with tomorrow's value and the model outputs the day after tomorrow's value. Bi-LSTM outperforms as compared to mentioned models. We finally selected the Bi-LSTM model for forecasting confirmed cases, death cases, and recovered cases for KSA.  
Figure 42 shows the infected forecasting after 30 days, the blue color represents the actual infected cases, and the red color represents the prediction cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Models** & \multicolumn{3}{c}{**Recovered Cases**} \\ \hline \hline \multirow{3}{*}{Saudi Arabia} & LSTM & 0.02 & 0.02 & 0.21 \\  & BI-LSTM & 0.02 & 0.02 & 0.21 \\ \hline \multirow{3}{*}{Bahrain} & LSTM & 0.045 & 0.044 & 0.24 \\  & BI-LSTM & 0.045 & 0.044 & 0.24 \\ \hline \multirow{3}{*}{Kuwait} & LSTM & 0.007 & 0.005 & 0.99 \\  & BI-LSTM & 0.019 & 0.016 & 0.93 \\ \hline \multirow{3}{*}{UAE} & LSTM & 0.194 & 0.17 & \(-\)1.17 \\  & BI-LSTM & 0.008 & 0.006 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of error measures of the DL models (LSTM, Bi-LSTM) for recovered cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Models** & \multicolumn{3}{c}{**Confirmed Cases**} \\ \hline \hline \multirow{3}{*}{Saudi Arabia} & LSTM & 0.034 & 0.033 & \(-\)3.38 \\  & Bi-LSTM & 0.026 & 0.026 & \(-\)1.69 \\ \hline \multirow{3}{*}{Bahrain} & LSTM & 0.051 & 0.050 & \(-\)0.05 \\  & Bi-LSTM & 0.051 & 0.050 & \(-\)0.05 \\ \hline \multirow{3}{*}{Kuwait} & LSTM & 0.012 & 0.011 & 0.96 \\  & Bi-LSTM & 0.031 & 0.028 & 0.76 \\ \hline \multirow{3}{*}{UAE} & LSTM & 0.205 & 0.193 & \(-\)0.96 \\  & Bi-LSTM & 0.023 & 0.0176 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of error measures of the DL models (LSTM, Bi-LSTM) for confirmed cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Models** & \multicolumn{3}{c}{**Death Cases**} \\ \hline \hline \multirow{3}{*}{Saudi Arabia} & LSTM & 0.02 & 0.02 & 0.87 \\  & Bi-LSTM & 0.018 & 0.017 & 0.87 \\ \hline \multirow{3}{*}{Bahrain} & LSTM & 0.05 & 0.05 & \(-\)1.01 \\  & Bi-LSTM & 0.05 & 0.05 & \(-\)1.01 \\ \hline \multirow{3}{*}{Kuwait} & LSTM & 0.004 & 0.003 & 0.99 \\  & Bi-LSTM & 0.023 & 0.02 & 0.87 \\ \hline \multirow{3}{*}{UAE} & LSTM & 0.264 & 0.25 & \(-\)4.77 \\  & Bi-LSTM & 0.0148 & 0.0125 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of error measures of the DL models (LSTM, Bi-LSTM) for death cases.  
Figure 43 shows the death forecasting after 30 days, the green color represents the actual death cases, and the red color represents the prediction cases.  
Figure 44 shows the recovered forecasting after 30 days, the blue color represents the actual recovered cases, and the red color represents the prediction cases.  
Figure 42: Infected forecasting after 30 days.  
Figure 43: Death forecasting after 30 days.