  The clinical spectrum of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2), the strain of coronavirus that caused the COVID-19 pandemic, is
broad, extending from asymptomatic infection to severe immunopulmonary
reactions that, if not categorized properly, may be life-threatening.
Researchers rate COVID-19 patients on a scale from 1 to 8 according to the
severity level of COVID-19, 1 being healthy and 8 being extremely sick, based
on a multitude of factors including number of clinic visits, days since the
first sign of symptoms, and more. However, there are two issues with the
current state of severity level designation. Firstly, there exists variation
among researchers in determining these patient scores, which may lead to
improper treatment. Secondly, researchers use a variety of metrics to determine
patient severity level, including metrics involving plasma collection that
require invasive procedures. This project aims to remedy both issues by
introducing a machine learning framework that unifies severity level
designations based on noninvasive saliva biomarkers. Our results show that we
can successfully use machine learning on salivaomics data to predict the
severity level of COVID-19 patients, indicating the presence of viral load
using saliva biomarkers.
