# In Vivo Cluster AI Prediction (CLAIRE) of COVID-19 Disease Progression

> **NIH NIH R43** · LIFETIME OMICS, INC. · 2021 · $248,723

## Abstract

ABSTRACT
The coronavirus COVID-19 pandemic, which early this year forced entire countries into
lockdown, has reached a global death toll of 890,000+ by early September 2020. Based on the
high number of COVID-19 cases that are asymptomatic but infectious, an estimated
reproductive rate of infection of about 2 and a high mutation rate, it is expected that the virus will
remain in the population as the influenza virus does. For hospitals serving areas whose
economy relies on international travel, tourism, and cruise ship tourism, such as Miami-Dade
county, new COVID-19 cases related to travel will require treatment during infection outbreaks
which will strain health systems, especially during the infectious respiratory disease season in
the winter. Patient risk factors during the current COVID-19 outbreak as well as during other
viral outbreaks, such as seasonal influenza, are poorly characterized, consequently negatively
affecting patient care. The saliva microbiome, which includes viruses and bacteria, is not
currently used as in diagnostic tools. However, it may reveal risk factors associated with severe
disease and/or a fatal outcome, and it allows for the detection and study of the viral RNA
sequence for potential contact tracing and molecular epidemiology, all of which affect both
vaccine and antiviral efficacy.
In this proposed study, Lifetime Omics will develop CLAIRE, a proof-of-concept in vivo cluster AI
platform for predicting disease progression of viral infectious respiratory diseases such as
COVID-19 through the analysis of the saliva metagenome. The University of Miami Medical
Group Infection Control (UMMGIC) division will collaborate in this effort by collecting saliva
samples from COVID-19 patients with de-identified clinical information. The samples will
undergo metagenomic sequencing and Lifetime Omics will repurpose algorithms used for
prediction of in vivo HIV evolution to perform genetic/phylogenetic analysis on SARS-CoV-2
RNA sequences, estimating mutation rate and immune selection pressures and identifying both
the in vivo quasispecies clusters and the geographic cluster to which the patient belongs. The
CLAIRE models will be trained with public datasets and tested on the metagenomic sequences
generated from saliva samples of UMMGIC patients with the goal of assisting physicians in
predicting disease progression in COVID-19.

## Key facts

- **NIH application ID:** 10256828
- **Project number:** 1R43EB030947-01A1
- **Recipient organization:** LIFETIME OMICS, INC.
- **Principal Investigator:** Patricia Buendia
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $248,723
- **Award type:** 1
- **Project period:** 2021-07-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10256828

## Citation

> US National Institutes of Health, RePORTER application 10256828, In Vivo Cluster AI Prediction (CLAIRE) of COVID-19 Disease Progression (1R43EB030947-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10256828. Licensed CC0.

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