# COVID-19 Outbreak Prediction with Machine Learning
## 3 Results
### Machine Learning Results  
This section presents the results for the training stage of ML methods. MLP and ANFIS were employed as single and hybrid ML methods, respectively. ML methods were trained using two datasets related to scenario 1 and scenario 2. Table 16 presents the results of the training phase. Figure 11: Set of models for USA fitted by GWO. Figure 10: Set of models for Germany fitted by GWO. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Scenario 1**} & \multicolumn{4}{c}{**Scenario 2**} \\ \hline  & \multicolumn{2}{c}{**MLP**} & \multicolumn{4}{c}{**ANFIS**} & \multicolumn{4}{c}{**MLP**} & \multicolumn{4}{c}{**ANFIS**} \\ \hline  & **No. of Neurons** & **r** & **RMSE** & **MF Type** & **r** & **RMSE** & **No. of Neurons** & **r** & **RMSE** & **MF Type** & **r** & **RMSE** \\ \hline \multirow{3}{*}{Italy} & 8 & 0.999 & 190.81 & Tri. & 0.999 & 189.76 & 8 & 0.999 & 199.52 & **Tri.** & **0.999** & **188.55** \\  & 12 & 0.999 & 194.84 & Trap. & 0.841 & 3743.63 & 12 & 0.999 & 195.79 & Trap. & 0.876 & 3276 \\  & **16** & **0.999** & **188.18** & Gauss & 0.998 & 320.93 & 16 & 0.999 & 195.2 & Gauss & 0.999 & 206.66 \\ \hline \multirow{3}{*}{China} & **Average** & **0.999** & **191.27** & & **0.946** & **1418.1** & **Average** & **0.999** & **196.83** & & **0.958** & **1223.73** \\  & 8 & 0.995 & 2287.55 & Tri. & 0.996 & 2293.09 & 8 & 0.996 & 2265.95 & Tri. & 0.996 & 2272.13 \\  & **12** & **0.996** & **2259.95** & Trap. & 0.987 & 4231.05 & 12 & 0.996 & 2285.73 & Trap. & 0.989 & 3835.34 \\  & 16 & 0.995 & 2407.16 & Gauss & 0.996 & 2283.3 & 16 & 0.996 & **2260.05** & Gauss & 0.996 & 2272.58 \\ \hline \multirow{3}{*}{Iran} & **Average** & **0.995** & **2318.22** & & **0.993** & **2960.81** & **Average** & **0.996** & **2270.57** & & **0.993** & **2793.35** \\  & 8 & 0.998 & 392.17 & Tri. & 0.998 & 395.33 & 8 & 0.998 & 404.21 & Tri. & 0.998 & 394.04 \\  & **12** & **0.998** & **391.04** & Trap. & 0.977 & 1282.33 & 12 & 0.998 & 392.77 & Trap. & 0.986 & 994 \\  & 16 & 0.998 & 392.19 & Gauss & 0.998 & 396.51 & 16 & 0.998 & 395.43 & **Gauss** & **0.998** & **391.96** \\ \hline \multirow{3}{*}{Germany} & **Average** & **0.998** & **391.8** & & **0.991** & **391.39** & **Average** & **0.998** & **397.47** & & **0.994** & **593.33** \\  & 8 & 0.999 & 55.6 & Tri. & 0.999 & 56.25 & 8 & 0.999 & 55.58 & Tri. & 0.999 & 55.63 \\  & **12** & **0.999** & **55.38** & Trap. & 0.12 & 168.87 & **12** & **0.999** & **55.56** & Trap. & 0.135 & 1537.26 \\  & 16 & 0.999 & 55.58 & Gauss & 0.998 & 154.99 & 16 & 0.999 & 55.56 & Gauss & 0.999 & 62.91 \\ \hline \multirow{3}{*}{USA} & **Average** & **0.999** & **55.52** & & **0.705** & **623.31** & **Average** & **0.999** & **55.56** & & **0.709** & **551.93** \\  & 8 & **0.999** & **21.65** & Tri. & 0.999 & 21.75 & 8 & 0.999 & 22.31 & Tri. & 0.999 & 22.52 \\  & 12 & 0.999 & 22.36 & Trap. & 0.22 & 861.08 & **12** & **0.999** & **22.3** & Trap. & 0.2 & 935.41 \\  & 16 & 0.999 & 22.31 & Gauss & 0.998 & 863.2 & 16 & 0.999 & 22.4 & Gauss & 0.999 & 22.503 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} USA \\ 16 \\ \end{tabular} } & **Average** & **0.999** & **22.1** & & **0.739** & **323.05** & **Average** & **0.999** & **22.33** & & **0.739** & **327.65** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Results for the training phase of the ML methods. According to Table 16, the datasets related to scenarios 1 and 2 have different performance values. Accordingly, for Italy, the MLP with 16 neurons provided the highest accuracy for scenario 1 and ANFIS with Tri. MF provided the highest accuracy for scenario 2. By considering the average values of the RMSE and correlation coefficient, it can be concluded that scenario 1 is more suitable for modeling outbreak cases in Italy because it provides higher accuracy (the smallest RMSE and the largest correlation coefficient) than scenario 2.