# COVID-19 Outbreak Prediction with Machine Learning
## 3 Results
### Machine Learning Results
For the dataset related to China, for both scenarios, MLP with 12 and 16 neurons, respectively for scenarios 1 and 2, provided the highest accuracy compared with the ANFIS model. By considering the average values of RMSE and correlation coefficient, it can be concluded that scenario 2 with a larger average correlation coefficient and smaller average RMSE than scenario 1 is more suitable for modeling the outbreak in China. For the dataset of Iran, MLP with 12 neurons in the hidden layer for scenario 1 and ANFIS with Gaussian MF type for scenario 2 provided the best performance for the prediction of the outbreak. By considering the average values of the RMSE and correlation coefficient, it can be concluded that scenario 1 provided better performance than scenario 2. In addition, in general, the MLP has higher prediction accuracy compared with the ANFIS method. In Germany, MLP with 12 neurons in its hidden layer provided the highest accuracy (smallest RMSE and largest correlation coefficient). By considering the average values of the RMSE and correlation coefficient, it can be concluded that scenario 1 is more suitable for the prediction of the outbreak in Germany than scenario 2. In the USA, the MLP with 8 and 12 neurons, respectively, for scenarios 1 and 2, provided higher accuracy (the smallest RMSE and the largest correlation coefficient values) than the ANFIS model. By considering the average values of the RMSE and correlation coefficient values, it can be concluded that scenario 1 is more suitable than scenario 2, and MLP is more suitable than ANFIS for outbreak prediction. Figures 12-16 present the model fits for Italy, China, Iran, Germany, and the USA, respectively. By comparing Figures 12-16 with Figures 7-11, it can be concluded that the MLP and the logistic model fitted by GWO provided a better fit than the other models. In addition, the ML methods provided better performance compared with other models. Figure 14: Set of models for Iran fitted by ML methods. Figure 13: Set of models for China fitted by ML methods. Comparing the Fitted Models  
This section presents a comparison of the accuracy and performance of the selected models for the prediction of 30 days' outbreak. Figures 17-21 show the deviation from the target values for the selected models. Figure 16: Set of models for USA fitted by ML methods. Figure 15: Set of models for Germany fitted by ML methods. Figure 19: Deviation from target value for models related to Iran. Figure 17: Deviation from target value for models related to Italy. Figure 18: Deviation from target value for models related to China. As is clear from Figures 17-21, the smallest deviation from the target values is related to the MLP for scenario 1 followed by MLP for scenario 2. This indicates the highest performance of the MLP method for the prediction of the outbreak. Figures 22-26 present the outbreak prediction for 75 days and Tables 17-21 present the outbreak prediction for 150 days. Figure 27 represents the dispersion of the outbreak for the countries studied in this paper. Figure 21: Deviation from target value for models related to USA. Figure 20: Deviation from target value for models related to Germany. Figure 14:0 The outbreak prediction for China through 75 days. Figure 14:1 The outbreak prediction for Italy through 75 days. Figure 14:2 The outbreak prediction for Iran through 75 days. Figure 14:3 The outbreak prediction for Germany through 75 days. Figure 14:4 An overview of the current state of the COVID-19 outbreak including daily cases for the four countries of the study (source: World Health Organization). Figure 14:5 The outbreak prediction for the USA through 75 days.