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. 2022 Apr 18;18:41. doi: 10.1186/s12992-022-00832-6

Table 2.

Feature ranking by origin and destination

Panel A Panel B
Corridor & Day dummies Corr. dum.
Features Linear KNN G-Boost MLP Avg. Avg.
Indiv. features
Origin - C1 School closures 100 69 100 59 82 85
Destin - C1 School closures 94 60 36 85 68 68
Destin - C3 Cancel public events 19 64 77 68 57 57
Origin - C3 Cancel public events 10 100 33 65 52 46
Origin - C7 Restr. Internal movement 0 93 16 100 52 51
Origin - H2 Testing policy 14 7 87 92 50 43
Origin - C4 Restrictions gatherings 50 51 40 54 49 44
Origin - C6 Stay home requirements 92 16 12 59 45 21
Destin - C6 Stay home requirements 17 15 96 54 45 45
Destin - C4 Restrictions gatherings 6 48 70 25 37 42
Destin - C7 Restr. Internal movement 13 66 12 58 37 29
Origin - H3 Contact tracing 5 12 27 44 22 22
Origin - C8 International travel bans 1 1 45 40 22 18
Origin - New Covid deaths 0 7 73 5 21 46
Origin - New Covid cases 11 0 21 52 21 75
Destin - New Covid deaths 6 10 64 0 20 49
Destin - C5 Close public transport 34 6 1 39 20 9
Destin - H3 Contact tracing 31 21 2 14 17 7
Destin - C2 Workplace closing 5 33 7 23 17 21
Destin - C8 International travel bans 0 12 20 32 16 24
Destin - New Covid cases 12 0 43 6 15 38
Origin - C5 Close public transport 18 7 0 31 14 12
Origin - C2 Workplace closing 1 23 10 14 12 23
Destin - H2 Testing policy 9 0 2 10 4 6
Synthetic features
Destin - Component 1 100 100 100 100 100 100
Origin - Component 1 13 75 20 49 39 48
Origin - Component 2 1 55 17 19 23 26
Destin - Component 2 9 50 0 0 15 17
Origin - New Covid cases 0 0 18 30 12 75
Destin - New Covid deaths 5 14 6 11 9 49
Origin - New Covid deaths 1 13 3 9 6 46
Destin - New Covid cases 0 2 1 6 2 38

Notes: The different features are ranked following the permutation importance method. For each approach, we provide results obtained with the model including day/corridor dummies (cols. 1-5) and the version including corridors dummies only (col. 6). Directional priors are always used to identify the effects of origin- and destination-specific features. The importance values of each feature is computed over 10 permutations using the negative mean absolute error (MAE). The resulted values are scaled between 0% and 100% separately for each model. The col. ‘Avg.’ averages the results obtained with the four learning techniques. The features are ranked according to the average importance of the models including the day/corridor dummies (Panel A). In Panel B, we only report the ‘Avg.’ score without reporting the model-specific results