Martin Z. Bazant’s COVID-19 app and MOOC expanding the reach of this research

May 25, 2021

Martin Z. Bazant’s focus on physics drives a new approach to modeling COVID-19 exposure risk, with an app and MOOC expanding the reach of this research

In spring 2020, Martin Z. Bazant, along with much of humanity, was watching the rapid spread of the Covid-19 outbreak with alarm. But Bazant, E. G. Roos (1944) Professor of Chemical Engineering and professor of applied mathematics, had cause for concern beyond the immediate, deadly impacts of SARS-CoV-2.

“From the beginning, I really felt that fear and emotion were driving much of the official response to the pandemic,” he says. “I was worried that public discourse, especially during a presidential election year, would reduce the role of science in pandemic decision-making.”

After reviewing early studies by other scientists on airborne transmission of the virus, Bazant found himself particularly troubled by public health policies advising surface cleaning and the “6-foot rule” of social distancing as the principle means of containing viral transmission, even when facemasks were worn.

“I thought this guidance might not be rooted in the principles of physical science and saw that there could be huge unintended consequences from implementing these policies uniformly to all indoor spaces,” he says. “I became alarmed that some spaces were still at high risk of airborne transmission over longer distances, such as nursing homes for the most vulnerable elderly population, while other spaces, such as schools or businesses, were perhaps being shut down unnecessarily.”

Seized by the urgency of elucidating the science of transmission, Bazant decided to add Covid-19 to an already bursting research portfolio.

This decision led, over the past year, to a pathbreaking, rigorous model for the airborne spread of SARS-CoV-2; an app based on this model that offers pragmatic guidelines for policymakers, businesses, schools and individuals for estimating the risks of exposure to the virus; and a MOOC (massive open online course) that engages thousands of users worldwide in understanding the science behind the model.

“I never imagined I would be doing this kind of work,” he says “But I felt I could not just sit here and complain, and that as a scientist, I had to take action,” he says.

Finding a new formula

 Public health advice early on signaled to Bazant considerable uncertainty about how SARS-CoV-2 spreads: Was transmission via both surface contamination and by airborne droplet? If primarily by air, as some scientists seemed to think, then what was the justification for the 6-foot rule?

“I was skeptical about this advice from the beginning,” Bazant says. In airplanes, where people are seated close together in a small space, air exchange and filtration has for the most part kept people safe from airborne infection for decades. And at MIT, in labs that handle toxic biological and chemical substances, large ventilation rates, high ceilings and fume hoods suffice to protect researchers, who are not directed to stand a certain distance apart.

To develop a more refined understanding of SARS-CoV-2 transmission, Bazant began consuming the expanding corpus of literature on superspreader events and other detailed infection case studies. “I set out to not to create a fancy simulation, but to extract a formula that would help people trying to find out what it takes to be safe in a particular room.”

Bazant entered chemical engineering as a professor of mathematics with a Ph.D. in physics and the desire to solve important problems in such areas as energy and sustainability. With the Covid-19 challenge, he employed the kind of data-driven modeling of physical processes he routinely turns to in his other research, and which he teaches in such core chemical engineering courses as 10.50 Analysis of Transport Phenomena.

With the help of John W. M Bush, a professor of applied mathematics who had previously worked on the fluid mechanics of coughing and sneezing, Bazant began zeroing in on the behavior of  tiny “aerosol” droplets containing the Covid-19 virus, ejected into the air of a room by people breathing, coughing, sneezing, talking, or singing. Studies showed that these droplets remain suspended for long periods of time, and mix throughout a room via air currents.

Given this scenario, the scientists believed that distancing was less relevant to reducing exposure than cumulative time spent in a space. With this in mind, they created a model that yields an estimate of how long it would take for someone to become infected with SARS-CoV-2 after an infected person enters the same indoor location. Their model factors in such parameters as the prevalence and type of masks, number of people and activity in a room, and ventilation and filtration systems. Although they quantified the role of physical distance in protecting against short-range transmission in “respiratory jets,” their analysis focused on long-range airborne transmission in well-mixed indoor spaces, building on classic epidemiological models.

“We did perhaps the most sophisticated analysis to date of such models, with greater detail of the roles of different respiratory activities than other studies,” says Bazant. Such details include the difference between breathing through the nose or mouth while sleeping, speaking at different volumes, singing, and the effects of relative humidity on viral deactivation and the size of droplets. The results of this research, first published September 1, 2020 in the health sciences preprint server, MedRxiv, appeared in the April 27 2021 PNAS paper, co-authored by Bazant and Bush.

A new guideline

Bazant hoped their scientifically rigorous model might prove a potential alternative or supplement to public health guidelines whose less nuanced take on airborne exposure could mean defining certain locations or activities as inherently high risk and subject to closure. So he leapt at opportunities to circulate his model more widely. Graduate students put him in touch with a software developer, Kasim Khan, who helped develop an open-access app and website that permit users to enter details of their specific location to determine likelihood of infection after a certain period of time.

“What’s really nice about it is it keeps the essential science in there, making it accessible to a broad range of people,” says Bazant. In the app’s basic mode, users plug in the number of people in a room of given dimensions, engaged in specific activities, to determine their risk of exposure over a period of time. In this mode, there are also presets for classrooms, airplanes and churches. The advanced mode allows users with knowledge about air flow rates, filtration and ventilation to enter variables for customized results.

The app, which went online in October, is updated regularly with new information, such as Covid-19 variants that are more infectious. It has attracted close to a million users across the globe to date, including a Washington state tennis club owner who appealed his facility’s Covid-based closure based on guidelines he generated using the app.

Another opportunity to introduce the Covid-19 model came courtesy of Joey Gu, Bazant’s former doctoral advisee, and now chemical engineering digital learning fellow. “Martin was engrossed in this research, and every time I met with him, he kept talking about his Covid work,” recalls Gu.  “In September 2020, I said ‘Why not make a MOOC?’”

Bazant, who leads the digital learning initiative in chemical engineering, had produced several online courses with Gu, and embraced the idea of a new edX class, “Physics of Covid-19 Transmission.”  Based on Bazant and Bush’s guidelines, as represented in the app, this self-paced course would lay out the physical principles behind airborne transmission of Covid-19, and show how to assess the risk of transmission. The only problem: to launch the course in the next edX cycle meant moving at breakneck speed, without external funding.

“The greatest challenge was the timeline,” says Gu. “Usually MOOCs take up to two years, but because of the urgency of the topic, we did everything in two months, ramping up production right away, making lots of videos, and questions to help users learn.”  In the class, Bazant lays out such fundamental concepts as fluid flow in a room and how to determine the concentration of virus in mixed air. “This has pedagogical value, because we’re showing the public how scientists approach a problem,” says Gu.  Nearly four thousand students have signed up to date, and many have engaged enthusiastically on the website forum, relaying their pandemic experiences.

“These Covid restrictions have affected every area of life, and until you start to talk to people and have this kind of outreach, you don’t realize all the impacts on them,” he says. “Many people are interested in keeping their places safe, reopening, and trying to base those decisions on some kind of science.”

Science-based public health

Even as the pandemic ebbs, Bazant and his colleagues perceive value in continuing and extending their Covid-19 work. Gu, for instance, would like to see the MOOC class become an MIT elective: “It is a nice demonstration of math modeling of physical concepts, and how this modeling allows you to tackle real life problems,” he says.

Bazant would like to reach a wider circle with his research. “By educating more and more people, I hope that we can eventually influence official guidance—not only for this pandemic, but the next one too.”

He continues refining the model and the app, incorporating new parameters such as community vaccination levels and indoor CO2 levels that serve as a proxy for airborne virus transmission risk. Bazant is also “getting interested in fluid mechanics of droplet formation in the body, and whether it enhances disease risk, which may be my way of entering the field of medicine,” he says.

Ultimately, he’d like to stake a firmer claim for the role of physical science and engineering in the public health-decision making process, and ensure that the rationale behind policy response to a disease is grounded in the best possible analysis.

“When something is an unknown risk, there’s no limit to how much precaution you will take because you don’t actually understand whether you’re safe or not. But the science tells me when I’m safe and how to make myself safe,” he says. “Knowledge is power.”

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