Researchers from the Massachusetts Institute of Technology (MIT) have developed a way of using carbon dioxide (CO2) monitors to help estimate the risk of catching Covid-19 and other airborne diseases in near real time.
The researchers said that it could help track the evolving risk of transmission in indoor spaces such as schools and offices and may also lay the groundwork for air quality monitoring systems of the future.
“CO2 monitoring has been used for decades to assess the quality of air handling in buildings, and can now be re-purposed to assess the risk of indoor airborne disease transmission, including Covid-19,” said Martin Z. Bazant, professor of chemical engineering and applied mathematics at MIT.
“We’ve shown how it can be used in conjunction with our safety guideline to assess that risk and hope to inform personal and policy decisions about closing and re-opening indoor spaces, such as schools and businesses,” he added.
The work has been detailed in the journal ‘Flow: Applications of Fluid Mechanics’.
Using data from super-spreader events, the team produced a mathematical model that estimated the average length of time it would take to become infected when sharing space with someone who had Covid-19.
From this they produced a safety guideline, setting limits on time spent in shared spaces, and adjusted by factors including the size of room, number of infected and susceptible people, what they were doing and whether ventilation and masks were in use.
The team combined measurements — on how much air people are breathing out and the rate at which it is removed by ventilation — with three models, which look at the dynamics of gas, of infectious aerosols — those virus-carrying droplets — and of disease transmission.
They stressed that concentrations of CO2 and airborne pathogens are not strictly linked, as the amount of virus in the air is affected by a number of factors, including the use of face masks.
So their model takes into account other variables that include not only masks, but ventilation, the use of air filtration, activity levels and the number of people likely to be infectious or susceptible to infection at different stages of a pandemic.
In all, more than 40 parameters feed into the model and produce an estimate of how much virus is present, and therefore the risk of infection, in near real-time.
The systems could predict transmission rates of airborne diseases like Covid-19 or seasonal flu, and work in concert with ventilation systems to adjust the air within buildings to keep the risk of transmission low, the researchers said.