Assessing indoor Covid transmission is now possible with the help of a model that MIT professors proposed. They have prepared a set of guidelines that include various parameters. These parameters include the size of the dwelling, the number of people in that dwelling, and activities they follow. Not just these but also does the dwelling have a proper ventilation system and do the inmates wear proper masks. So, the two MIT professors involved in curating these guidelines claim them to be physics-based.
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Assessing indoor Covid transmission-the underlying theory
The two MIT professors involved in developing the physics-based model are Martin Z.Bazant and John W.M.Bush. While they both belong to the applied mathematics department, Mr. Bazant is also a professor of chemical engineering. So, the physics these professors explain about an indoor setting is that there are numerous tiny airborne droplets enclosed. As people in a closed setting talk, sneeze, sing or even eat, pathogens get attached to tiny airborne droplets. Thus assessing indoor Covid transmission depending on various factors is important. Because, the 6-feet rule that people follow in the exterior environment, does not go well with the interiors, thus the two professors chalked out a new model.
Quantitative approach
Based on the data available for various parameters, the model estimates the time that how long it would take for a person to get contaminated with the SARS-CoV-2 virus. The interesting part about assessing indoor Covid transmission with this quantitative approach is that it provides a guide rather than yes/no answers. Guide like for how long a person can expect to be safe in an indoor setting when an infected person enters and remains in the said area.
Quotes from these professors:
Bazant says, “As scientists, we’ve tried to be very thoughtful and only go with what we see as hard data. We feel confident that we’ve made conservative choices at every point.” The team duo is very confident about their rigorous work. And is working relentlessly with the new data that appears every day.
