Table 2: Selecting best model for forecasting.
| Models | New Deaths | New Deaths | Total Deaths | Total Deaths |
| ARIMA(P,D,Q) / SARIMA(P,D,Q)(p,d,q) | ARIMA (5,2,10) | SARIMA (8,2,5)(3,2,2) | ARIMA (3,2,3) | SARIMA (3,2,3)(2,0,0) |
| AIC | -938.8490 | -787.8707 | -929.0862 | -847.4050 |
| SC | -882.4410 | -722.9302 | -902.5412 | -815.0939 |
| RMSE | 0.021813 | 0.022029 | 0.022909 | 0.023797 |
| MAE | 0.0092537 | 0.0091518 | 0.0085919 | 0.0091842 |
| MAPE | 0.83075 | 0.026914 | 1.0359 | 0.037849 |
| THEIL'S U | 0.1067 | 0.0039832 | 0.013931 | 0.002315 |
| error is normally distributed | No | No | No | No |
| no ARCH effect is present | Yes | Yes | Yes | Yes |
| no autocorrelation in the residuals | Yes | Yes | Yes | Yes |
| Models | New Cases | New Cases | Total Cases | Total Cases |
| ARIMA(P,D,Q)/SARIMA(P,D,Q)(p,d,q) | ARIMA (6,2,10) | SARIMA (5,2,3)(1,1,1) | ARIMA (10,2,2) | SARIMA (7,2,3)(1,1,1) |
| AIC | -60.89161 | -41.61947 | -476.8320 | -1045.578 |
| SC | -4.483565 | -5.504226 | -434.3499 | -1003.854 |
| RMSE | 0.17977 | 0.19828 | 0.066212 | 0.012949 |
| MAE | 0.10122 | 0.11315 | 0.032833 | 0.0078238 |
| MAPE | 0.010639 | 0.010793 | 0.00014523 | 5.643e-005 |
| THEIL'S U | 0.00074624 | 0.00060478 | 1.1487e-005 | 4.079e-006 |
| error is normally distributed | No | No | No | No |
| no ARCH effect is present | No | No | Yes | No |
| no autocorrelation in the residuals | Yes | Yes | No | Yes |