Fig. 2

Random Forest regression analysis. A Variable importance resulting from Random Forest model. Variable importances are expressed in terms of the rise in MSE resulting from the removal of the variable (namely %IncMSE, y-axis) and the increase in residual sum of squares attributable to the exclusion of the variable (namely IncNodePurity, sphere dimension). Top 5 selected important variables are marked with an asterisk (*). B–E Interaction between top important variables and time since first vaccine dose within the Random Forest model. 3D partial dependence plots are generated by plotting the predicted response [anti-S IgG expressed in binding antibody units per milliliter (BAU/mL)] on the z-axis as two variables are changed (all other variables held at their median/mode values). Numerical variables are normalized using z-score. Red arrows indicate the number of vaccine doses received. For each pair of parameters two different visualizations were obtained by rotating the plot around the z-axis with an angle of 80°. The interaction of time since first dose administration, reported on the x-axis, versus previous SARS-CoV-2 infection (B), BMI (C), CD4 T-cell count (D), and CD4/CD8 ratio (E). BMI body mass index, COPD chronic obstructive pulmonary disease, IDU injective drug use, MSM men who have sex with men, MSW men who have sex with women, WSM women who have sex with men, AIDS acquired immunodeficiency syndrome, ART antiretroviral therapy, INSTI integrase strand-transfer inhibitor, PI protease inhibitor, NNRTI non-nucleoside reverse transcriptase inhibitor