Machine learning-based classification of COVID-19 severity using respiratory microbiome profiles from shotgun
Eli Gabriel Avina-Bravo1,2, Isabel García-Lorenzo3, Mariel Alfaro-Ponce1,2
1Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey, Mexico.
Frontiers in Bioinformatics
|June 22, 2026
Summary
An AI tool using respiratory microbiome data accurately triages COVID-19 patients. Machine learning models identified microbial signatures linked to disease severity, aiding clinical decision-making during outbreaks.
Area of Science:
- Microbiology
- Computational Biology
- Infectious Disease Research
Background:
- Accurate clinical triage is crucial for managing infectious disease outbreaks like COVID-19.
- Respiratory microbiome analysis offers potential for patient stratification.
Purpose of the Study:
- To develop an AI-driven decision-support tool for COVID-19 patient triage.
- To identify respiratory microbiome profiles associated with disease severity and outcomes.
Main Methods:
- Analyzed 477 shotgun respiratory metagenomes from three independent cohorts.
- Integrated genus-level taxonomic profiles with clinical metadata (age, sex, antibiotic exposure).
- Trained and evaluated supervised machine learning models (Random Forest, SVM, XGBoost) using cross-validation and hyperparameter tuning.
Main Results:
- Observed a shift from commensal to dysbiotic respiratory microbiomes in severe/deceased COVID-19 patients, with enrichment of *Acinetobacter* and *Staphylococcus*.
- XGBoost model achieved high performance (up to 96.1% accuracy, 98.2% ROC-AUC) across cohorts and on integrated data.
- Identified key microbial predictors, with reduced-feature models retaining significant discriminative power.
Conclusions:
- Respiratory microbiome profiles can inform outcome-oriented clinical triage for COVID-19.
- Microbiome-informed machine learning provides a scalable and interpretable decision-support approach for infectious disease management.
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