Aggregate network representations of fitted submodels showcasing predictive capacity—aggregate network for submodel (A) y1 and (B) y2. The edge weights can be interpreted as predictive capacity for COVID severity. For y1, positive values indicate that an increase in perturbation results in an increased likelihood that a sample is classified as y2 (i.e. infected) relative to uninfected. For y2, positive values indicate that an increase in perturbation results in an increased likelihood that a sample is classified as severe relative to mild/moderate. Node size is proportional to weighted degree as a measurement of network connectivity and, by extension, biomarker importance. C) ASVs in the SSPN as proportions of presence in the various conditions, demonstrating that the majority of the ASVs are due to differences in abundances of common taxa across all conditions, and not rare species.