Washington University Researchers Develop a Real-Time Air Monitor Device to Detect COVID Variants in 5 Minutes

This cutting-edge COVID-19 detecting device can be used in hospitals and healthcare facilities, schools and public places to help detect SARS-CoV-2. It can further monitor for detecting other respiratory virus aerosols, such as influenza and respiratory syncytial virus (RSV)

The US Washington University researchers have developed an advanced real-time air monitor that can detect the SARS-CoV-2 virus variant within a room in five minutes. 

This device was formulated by combining recent advances in aerosol sampling technology and an ultrasensitive biosensing technique. This cutting-edge COVID-19 detecting device can be used in hospitals and healthcare facilities, schools and public places to help detect SARS-CoV-2.

Claimed to be the most sensitive detector, the device can further monitor for detecting other respiratory virus aerosols, such as influenza and respiratory syncytial virus (RSV). The findings through the monitor shall be published in the Journal Nature Communications.

John Cirrito, Professor of Neurology at Washington University, said, "If you are in a room with 100 people, you don't want to find out five days later whether you could be sick or not. The idea with this device is that you can know essentially in real-time or every 5 minutes if there is a live virus." 

Earlier, the Washington researchers launched a micro-immuno electrode (MIE) biosensor that detects amyloid beta as a biomarker for Alzheimer's disease. Therefore, efforts were made for it to be converted into a detector for SARS-COV-2

Reports suggest that the researchers exchanged the antibody that recognises amyloid beta for a nanobody from llamas that recognises the spike protein from the SARS-COV-2 virus to achieve the objective. 

While monitoring the apartments of two COVID-positive patients, the team of researchers tested the device which successfully detected RNA of the virus in the air samples from the bedrooms but did not detect any in the control air samples.

However, later in laboratory experiments, the biosensor was able to detect varying levels of airborne virus concentrations within a few minutes of sampling. 

In March 2023, Washington University Researchers developed a susceptible-exposed-infectious (SEI) dynamic model for forecasting COVID-19 trajectories under various interventions and SARS-CoV-2 variant scenarios. Using this model, the researchers estimated historical infections, hospitalizations, and deaths by location, day, and variant.