Table 6.
Feature ranking including lagged epidemiological conditions
| Panel A: Corridor & Day dummies | Panel B: Corridor dummies | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Features | Linear | KNN | G-Boost | MLP | Avg. | Linear | KNN | G-Boost | MLP | Avg. |
| C1 School closing | 100% | 69% | 81% | 68% | 79% | 100% | 83% | 79% | 97% | 90% |
| C3 Cancel public events | 10% | 100% | 56% | 78% | 61% | 19% | 100% | 58% | 77% | 63% |
| C7 Restr. Internal movement | 5% | 89% | 22% | 100% | 54% | 4% | 74% | 20% | 100% | 49% |
| C6 Stay home requirements | 60% | 20% | 79% | 55% | 54% | 27% | 24% | 74% | 31% | 39% |
| C4 Restrictions gatherings | 12% | 57% | 100% | 33% | 51% | 5% | 74% | 100% | 32% | 53% |
| H2 Testing policy | 12% | 6% | 69% | 54% | 35% | 20% | 2% | 67% | 29% | 30% |
| H3 Contact tracing | 23% | 15% | 37% | 29% | 26% | 21% | 0% | 34% | 34% | 22% |
| C5 Close public transport | 25% | 11% | 7% | 32% | 19% | 14% | 5% | 4% | 33% | 14% |
| C8 Inter. travel controls | 0% | 7% | 30% | 28% | 16% | 11% | 16% | 28% | 21% | 19% |
| C2 Workplace closing | 0% | 32% | 8% | 19% | 15% | 2% | 41% | 5% | 12% | 15% |
| New Covid cases t−7 | 12% | 0% | 8% | 12% | 8% | 14% | 34% | 6% | 46% | 25% |
| New Covid deaths t | 4% | 12% | 12% | 0% | 7% | 10% | 49% | 13% | 12% | 21% |
| New Covid cases t | 1% | 1% | 0% | 24% | 7% | 4% | 41% | 0% | 68% | 28% |
| New Covid cases t−14 | 4% | 1% | 9% | 8% | 6% | 6% | 33% | 8% | 0% | 12% |
| New Covid deaths t−14 | 0% | 12% | 6% | 5% | 6% | 0% | 53% | 7% | 21% | 20% |
| New Covid deaths t−7 | 1% | 10% | 4% | 3% | 5% | 8% | 48% | 4% | 44% | 26% |
Notes: The different features are ranked following the permutation importance method. The importance values of each feature is computed over 10 permutations using the negative mean absolute error (MAE). Four new variables are inserted in addition to the ones includes in the regression: 7- and 14-days of new Covid cases and deaths are included. For each approach, we provide results obtained with the model including day/corridor dummies (cols. 1-5) and the version including corridors dummies only (cols. 6-10). Directional priors are not included. The origin- and destination-specific features importance are aggregated by taking the mean of the 2. Finally, the resulted values are scaled between 0% and 100% separately for each model. The last column in each panel presents the mean value of importance averaged over the four models. The features are ranked according to the average importance of the models including the corridor and day dummies