Table 2 The average AUC score. This is accurate enough to find the principle of our SEIR model, the ranking model of all data, on the condition that we eliminate the people with state I that do not have any surrounding features measured in up to three layers. The prediction divided people into two groups by probability of 0.5: either they will or will not be infected. The result of feature importance shows the contact time to the high-risk location which is more relevant for judging whether someone has been infected or not, and where \(X_I\) and \(X_E\) are the total numbers of state I and E in the first three surrounding layers, respectively.

From: Continuous learning and inference of individual probability of SARS-CoV-2 infection based on interaction data

  Average AUC of our model
Score 0.96
Feature importance
Result [\(X_{\Delta Time}\), \(X_{\Delta Distance}\), \(X_{I}\), \(X_{E}\)]
[0.48, 0.13, 0.22, 0.23 ]