# COVID-19 Outbreak Prediction with Machine Learning
## 3 Results  
Tables 3-10 present the results of the accuracy statistics for the logistic, linear, logarithmic, quadratic, cubic, compound, power, and exponential equations, respectively. The coefficients of each equation were calculated by the three ML optimizers; GA, PSO, and GWO. The table contains country name, model name, population size, number of iterations, processing time, RMSE, and correlation coefficient. Figure 6: Adaptive neuro fuzzy inference system (ANFIS) architecture. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **Model** & **Pop. Size** & **Iteration** & **Processing Time** & **RMSE** & **Correlation Coefficient** \\ \hline \multirow{3}{*}{Italy} & GA & 300 & 500 & 92 s & 3774.06 & 0.845 \\  & PSO & 1000 & 500 & 42 s & 3645.76 & 0.844 \\  & GWO & 500 & 1000 & 16 s & 3642.44 & 0.844 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 91 s & 7188.95 & 0.981 \\  & PSO & 1000 & 500 & 39 s & 6644.16 & 0.982 \\  & GWO & 500 & 1000 & 14 s & 5039.48 & 0.982 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 96 s & 3330.45 & 0.943 \\  & PSO & 1000 & 500 & 45 s & 2072.71 & 0.944 \\  & GWO & 500 & 1000 & 18 s & 1981.97 & 0.944 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 88 s & 850.22 & 0.745 \\  & PSO & 1000 & 500 & 40 s & 596.69 & 0.746 \\  & GWO & 500 & 1000 & 17 s & 592.48 & 0.746 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 93 s & 1118.77 & 0.758 \\  & PSO & 1000 & 500 & 47 s & 964.46 & 0.759 \\ \cline{1-1}  & GWO & 500 & 1000 & 20 s & 951.63 & 0.759 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy statistics for the linear model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **Model** & **Pop. Size** & **Iteration** & **Processing Time** & **RMSE** & **Correlation Coefficient** \\ \hline \multirow{3}{*}{Italy} & GA & 300 & 500 & 82 s & 1028.98 & 0.996 \\  & PSO & 1000 & 500 & 36 s & 3358.1 & 0.997 \\  & GWO & 500 & 1000 & 14 s & 187.15 & 0.999 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 79 s & 42,160.4 & 0.982 \\  & PSO & 1000 & 500 & 35 s & 2524.44 & 0.994 \\  & GWO & 500 & 1000 & 13 s & 2270.58 & 0.995 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 81 s & 1267.04 & 0.992 \\  & PSO & 1000 & 500 & 36 s & 628.62 & 0.997 \\  & GWO & 500 & 1000 & 13 s & 392.88 & 0.996 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 82 s & 1028.98 & 0.999 \\  & PSO & 1000 & 500 & 38 s & 350.33 & 0.999 \\  & GWO & 500 & 1000 & 15 s & 22.35 & 0.999 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 86 s & 5339.5 & 0.983 \\  & PSO & 1000 & 500 & 39 s & 555.32 & 0.997 \\ \cline{1-1}  & GWO & 500 & 1000 & 16 s & 555.54 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy statistics for the logistic 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 & 98 s & 8325.33 & 0.634 \\  & PSO & 1000 & 500 & 51 s & 8818.2 & 0.634 \\  & GWO & 500 & 1000 & 20 s & 9296.59 & 0.634 \\ \hline \multirow{3}{*}{China} & GA & 300 & 500 & 96 s & 40,828.2 & 0.847 \\  & PSO & 1000 & 500 & 42 s & 43,835.37 & 0.847 \\  & GWO & 500 & 1000 & 17 s & 42,714.93 & 0.847 \\ \hline \multirow{3}{*}{Iran} & GA & 300 & 500 & 102 s & 4929.97 & 0.757 \\  & PSO & 1000 & 500 & 59 s & 8775.56 & 0.757 \\  & GWO & 500 & 1000 & 22 s & 8995.52 & 0.756 \\ \hline \multirow{3}{*}{USA} & GA & 300 & 500 & 94 s & 889.15 & 0.538 \\  & PSO & 1000 & 500 & 37 s & 1130.33 & 0.538 \\ \cline{1-1}  & GWO & 500 & 1000 & 15 s & 1135.12 & 0.538 \\ \hline \multirow{3}{*}{Germany} & GA & 300 & 500 & 95 s & 1552.22 & 0.548 \\ \cline{1-1}  & PSO & 1000 & 500 & 45 s & 1966.81 & 0.548 \\ \cline{1-1}  & GWO & 500 & 1000 & 21 s & 1878.67 & 0.548 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy statistics for the logarithmic model.