Data-driven research is one of the key weapons in the fight against COVID-19. Cloud technology such as Texas A&M Secure Technologies for Aggie Researchers (STAR) is becoming a game-changer for many, since it brings artificial learning (AI) and machine learning to the battle.

One research project taking advantage of STAR is part of the Prevent COVID U program through the COVID-19 Prevention Network (CoVPN), which was formed by the National Institute of Allergy and Infectious Diseases (NIAID) at the US National Institutes of Health (NIH). The project is studying breakthrough infections and spread among 18-29 year olds who received the Moderna vaccine.

Joy Alonzo, clinical assistant professor at the Texas A&M Irma Lerma Rangel College of Pharmacy, said Texas A&M is one of 22 sites participating in COVID U, but is the largest with over 500 participants.

“We are studying transmission rates and not the efficacy of the vaccine,” she explained. “What we're looking for is data that no one has, which are features of a breakthrough infection and how you can detect them right away, before the person knows they are ill. We have a theory that your pulse oximetry or oxygen saturation might be a little low or your heart rate might be a little high, so we're looking for the key symptoms someone may have before they actually feel sick.”

Alonzo pointed to preliminary data showing that a significant percentage of those vaccinated have asymptomatic infections.

“Such patients used to be referred to as ‘carriers,’” she said. “The big question is do these vaccinated patients transmit the infection to other vaccinated people and can they transmit to the unvaccinated. So, we want to identify the transmission rates and detect those breakthrough infections or asymptomatic positives as quickly as possible.”

Participants agree to take daily COVID-19 tests at home and use a mobile app called COVID Key, which was originally created to regularly test student athletes before vaccines were available. The app is also connected to a Theora Clear Device, an innovative mobile kiosk developed in partnership with Texas A&M Health, the Texas A&M Engineering Experiment Station and Austin-based company Clairvoyant Networks. The device utilizes a specialized camera created to detect temperature, the amount of oxygen in a person’s blood and their heart rate. The camera can also take a photo inside the person’s eye to detect conjunctivitis, commonly known as “pink eye” — another possible predictor of COVID infection. These clinical markers can be correlated with data in the STAR environment about the participant’s COVID-19 status to discover correlations indicating early infection.

Since the data collected for COVID U contains PII, it must be stored on a HIPAA-approved service. Alonzo explained that the tools provided by STAR and Amazon Web Services (AWS) were crucial to the project since Texas A&M previously didn’t have a HIPAA-compliant database. The tools also dynamically generate QR codes that allow participants to self-authenticate using the mobile app, ensuring the security of the field data collection.

Alonzo said that while some researchers may be apprehensive about using the cloud, she has seen the many advantages of distributed computing.

“I think a lot of researchers could benefit from the power of STAR and the cloud, but may not realize how they can apply that power to their project,” Alonzo said. “The AWS and STAR environments are designed to work with large data sets and do the analysis so you don’t have to do the correlations yourself. They are now crucial to my work.”