# COVID-19 Outbreak Prediction with Machine Learning
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Pop. Size** & **Iteration** & **Processing Time** & **RMSE** & **Correlation Coefficient** \\ \hline \multirow{3}{*}{Italy} & GA & 300 & 500 & 102 s & 6710.01 & 0.976 \\  & PSO & 1000 & 500 & 54 s & 5102.4 & 0.953 \\  & GWO & 500 & 1000 & 26 s & 1272.1 & 0.982 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 100 s & 7921.33 & 0.992 \\  & PSO & 1000 & 500 & 46 s & 4328.71 & 0.993 \\  & GWO & 500 & 1000 & 20 s & 3710.16 & 0.993 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 105 s & 6771.74 & 0.995 \\  & PSO & 1000 & 500 & 62 s & 822.09 & 0.998 \\  & GWO & 500 & 1000 & 24 s & 310.02 & 0.998 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 98 s & 754.6 & 0.931 \\  & PSO & 1000 & 500 & 38 s & 791.92 & 0.853 \\  & GWO & 500 & 1000 & 19 s & 307.58 & 0.938 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 101 s & 7577 & 0.904 \\  & PSO & 1000 & 500 & 49 s & 752.95 & 0.923 \\ \cline{1-1}  & GWO & 500 & 1000 & 26 s & 472.62 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy statistics for the quadratic model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Pop. Size** & **Iteration** & **Processing Time** & **RMSE** & **Correlation Coefficient** \\ \hline \multirow{3}{*}{Italy} & GA & 300 & 500 & 112 s & 7973.11 & 0.993 \\  & PSO & 1000 & 500 & 61 s & 4827.08 & 0.996 \\  & GWO & 500 & 1000 & 34 s & 324.33 & 0.998 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 113 s & 15,697.84 & 0.971 \\  & PSO & 1000 & 500 & 59 s & 3611.15 & 0.995 \\  & GWO & 500 & 1000 & 34 s & 2429.45 & 0.995 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 120 s & 5852.66 & 0.995 \\  & PSO & 1000 & 500 & 88 s & 3809.76 & 0.997 \\  & GWO & 500 & 1000 & 39 s & 250.2 & 0.999 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 110 s & 37,766.56 & 0.875 \\  & PSO & 1000 & 500 & 49 s & 678.36 & 0.979 \\  & GWO & 500 & 1000 & 25 s & 118.24 & 0.991 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 116 s & 1709.06 & 0.744 \\  & PSO & 1000 & 500 & 59 s & 1812.78 & 0.967 \\ \cline{1-1}  & GWO & 500 & 1000 & 29 s & 196.8 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy statistics for the cubic model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Pop. Size** & **Iteration** & **Processing Time** & **RMSE** & **Correlation Coefficient** \\ \hline \multirow{3}{*}{Italy} & GA & 300 & 500 & 102 s & 6710.01 & 0.976 \\  & PSO & 1000 & 500 & 54 s & 5102.4 & 0.953 \\  & GWO & 500 & 1000 & 26 s & 1272.1 & 0.982 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 100 s & 7921.33 & 0.992 \\  & PSO & 1000 & 500 & 46 s & 4328.71 & 0.993 \\  & GWO & 500 & 1000 & 20 s & 3710.16 & 0.993 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 105 s & 6771.74 & 0.995 \\  & PSO & 1000 & 500 & 62 s & 822.09 & 0.998 \\  & GWO & 500 & 1000 & 24 s & 310.02 & 0.998 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 98 s & 754.6 & 0.931 \\  & PSO & 1000 & 500 & 38 s & 791.92 & 0.853 \\ \cline{1-1}  & GWO & 500 & 1000 & 19 s & 307.58 & 0.938 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 101 s & 7577 & 0.904 \\ \cline{1-1}  & PSO & 1000 & 500 & 49 s & 752.95 & 0.923 \\ \cline{1-1}  & GWO & 500 & 1000 & 26 s & 472.62 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy statistics for the compound model. According to Tables 3-10, GWO provided the highest accuracy (smallest RMSE and largest correlation coefficient) and smallest processing time compared to PSO and GA for fitting the logistic, linear, logarithmic, quadratic, cubic, power, compound, and exponential equations for all five countries. It can be suggested that GWO is a sustainable optimizer due to its acceptable processing time compared with PSO and GA. Therefore, GWO was selected as the best optimizer by providing the highest accuracy values compared with PSO and GA.