Table 2 Short-term forecasting performance in the context of the SARS outbreak in Singapore. The sub-epidemic model outperformed the simpler growth models in terms of all of the performance metrics in short-term forecasts. Values highlighted in italics correspond to the best performance metric at a given forecasting horizon
From: A novel sub-epidemic modeling framework for short-term forecasting epidemic waves
Model | Mean absolute error (MAE) | Mean squared error (MSE) | Mean interval score (MIS) | Percentage coverage of the 95% prediction interval |
|---|---|---|---|---|
4 days ahead | ||||
 Sub-epidemic wave | 3.6 | 28.1 | 40.6 | 76.1 |
 Richards | 3.7 | 28.8 | 79.1 | 63.3 |
 Logistic | 3.8 | 31.1 | 60.3 | 69.4 |
6 days ahead | ||||
 Sub-epidemic wave | 4.0 | 39.5 | 46.9 | 76.3 |
 Richards | 4.1 | 39.7 | 87.9 | 60.4 |
 Logistic | 4.1 | 42.0 | 66.0 | 69.3 |
8 days ahead | ||||
 Sub-epidemic wave | 4.4 | 55.7 | 54.1 | 75.6 |
 Richards | 4.4 | 54.5 | 94.7 | 59.4 |
 Logistic | 4.4 | 56.9 | 71.1 | 68.9 |
10 days ahead | ||||
 Sub-epidemic wave | 4.9 | 83.5 | 60.3 | 74.0 |
 Richards | 4.8 | 79.3 | 99.0 | 58.9 |
 Logistic | 4.8 | 81.7 | 77.2 | 68.0 |