# An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning
## 4 Results
### Results of the Machine Learning Models  
This subsection trains the algorithms' linear regression, PR, and SVR for each country individually. For confirmed case predictions and recovered case predictions, two attributes were used: observation date and the cumulative number of confirmed cases or recovered cases. Furthermore, for the prediction of death cases, two attributes, observation date and the cumulative number of death cases in the existing data of each country, were used. As a result, the ML model fits the actual data with different performance metrics, as reported in Tables 4-6. The regression results of the confirmed cumulative recovered and cumulative death cases are illustrated in the figures below. Figure 6 shows the fit of the ML models for KSA infected cases, and the polynomial regression model is considered the optimal ML model in fitting the actual data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Models** & \multicolumn{3}{c}{**Confirmed Cases**} \\ \hline \hline \multirow{4}{*}{Saudi Arabia} & \multicolumn{1}{c}{Licner Regression} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \cline{2-5}  & \multicolumn{1}{c}{Licner Regression} & 48,514.63 & 48,512.25 & \(-\)103.62 \\  & PR & 3961.99 & 2754.49 & 0.30 \\  & SVR & 119,299.83 & 117,811.78 & \(-\)631.6 \\ \hline \multirow{4}{*}{Bahrain} & \multicolumn{1}{c}{Licner Regression} & 6307.73 & 6299.25 & \(-\)17.55 \\  & PR & 13,489.85 & 11,589.41 & \(-\)83.85 \\  & SVR & 54,273.51 & 54,779.55 & \(-\)1398.2 \\ \hline \multirow{4}{*}{Kuwait} & \multicolumn{1}{c}{Licner Regression} & 7842.52 & 7826.75 & \(-\)10.24 \\  & PR & 1350.10 & 1274.91 & 0.66 \\ \cline{1-1}  & SVR & 88,824.25 & 88,037.89 & \(-\)1441.0 \\ \hline \multirow{4}{*}{UAE} & \multicolumn{1}{c}{Licner Regression} & 61,598.57 & 60,376.96 & \(-\)15.23 \\ \cline{1-1}  & PR & 5095.28 & 4553.26 & 0.88 \\ \cline{1-1}  & SVR & 56,183.45 & 56,085.20 & \(-\)12.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of machine learning models (linear regression, PL, SVR) in terms of different error measures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Countries** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Saudi Arabia & 170,033.74 & 135,132.88 & \(-\)0.45 \\ Bahrain & 56,518.49 & 44,547.75 & \(-\)1.64 \\ Kuwait & 44,103.03 & 4969.99 & 0.35 \\ UAE & 102,177.12 & 74,681.14 & \(-\)1.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of SIR model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Countries** & **Models** & \multicolumn{3}{c}{**Death Cases**} \\ \hline \hline \multirow{4}{*}{Saudi Arabia} & \multicolumn{1}{c}{**RMSE**} & \multicolumn{1}{c}{**MAE**} & \multicolumn{1}{c}{**R\({}^{2}\)**} \\ \cline{2-5}  & \multicolumn{1}{c}{Licner Regression} & 709.106 & 702.039 & \(-\)803.79 \\  & PR & 551.60 & 467.63 & \(-\)485.99 \\  & SVR & 4014.08 & 3970.29 & \(-\)25,788.01 \\ \hline \multirow{4}{*}{Bahrain} & \multicolumn{1}{c}{Licner Regression} & 33.25 & 32.97 & \(-\)130.4 \\  & PR & 114.30 & 97.44 & \(-\)1551.54 \\ \cline{1-1}  & SVR & 273.28 & 270.84 & \(-\)8874.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of machine learning models (linear regression, PL, SVR) in different error measures. Figure 7 shows the fit of the ML models for Bahrain infected cases; the linear regression model is considered the optimal ML model in fitting the actual data.