An air of uncertainty descended on MIT’s campus in early March. Whispers and rumors about campus closing down swirled in the hallways. Students convened en masse on Killian Court to dance, hug, and cry as they were told they had until the end of the week to vacate campus. Within days, the Infinite Corridor’s usual stream of activity and noise was silenced.
While MIT’s dorms and classrooms became unnervingly quiet, there was a thrum of activity among faculty and researchers. Research teams across the Institute quickly swung into action, hatching plans and developing technologies to slow or stop the spread of the virus. These teams were among the only people allowed on campus this spring to work on Covid-19 related research.
The unprecedented nature of this global pandemic necessitates a diverse range of solutions. From designing low-cost ventilators to understanding how the virus is transmitted and manufacturing PPE, mechanical engineers have been a driving force in many research projects that seek to slow Covid-19’s spread and save lives.
“Mechanical engineers are used to developing concrete solutions for the grand challenges the world faces across a vast range of research areas,” says Evelyn Wang, Gail E. Kendall Professor and head of MIT’s Department of Mechanical Engineering. “This uniquely positioned our research community to serve as leaders in the global response to the Covid-19 pandemic.”
Since the beginning of the year, a number of mechanical engineering faculty and research staff at MIT have led collaborative research efforts in the fight against the virus. These projects have had a tangible impact — deepening our understanding of how the virus spreads, informing international guidelines, and protecting front-line workers and vulnerable populations.
Predicting the spread with machine learning
Earlier this year, as coronavirus cases spiked in countries like Italy, South Korea, and the United States, two main questions emerged: How many cases would there be in each country and what measures could be taken to stop the spread? George Barbastathis, professor of mechanical engineering, worked with Raj Dandekar, a PhD candidate studying civil and environmental engineering, to develop a model that could answer these questions.
The pair created the first-ever model that combined data from the spread of Covid-19 with a neural network to make predictions about the spread and determine which quarantine measures were effective. Dandekar first began developing the model as a project for MIT course 2.168 (Learning Machines), which Barbastathis teaches. He was inspired by a mathematical approach developed by Christopher Rackauckas, instructor of mathematics at MIT, that was published on a pre-print server in January of this year.
“I found it really interesting working in this new field of scientific machine learning, which combines machine learning with the physical world using real-life data,” says Dandekar. Their model enhanced the traditional SEIR model, which captures the number of “susceptible,” “exposed,” “infected,” and “recovered” individuals, by training a neural network to also identify those who were under quarantine and therefore no longer at risk to spread the virus. Using data after the 500th case was recorded in Wuhan, China; Italy; South Korea; and the United States, Barbastathis and Dandekar mapped the spread of the virus and derived what is known as the “quarantine control strength function.”
The result, perhaps unsurprisingly, demonstrated that the stronger the quarantine measures, the more effective a country was in slowing or stopping the spread. After releasing their model open-source on the web, Barbastathis reflected on the second wave that had just hit South Korea during an interview in early April.
“If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic,” Barbastathis said at the time. Weeks later, many states in the United States found these words to ring true as cases spiked.
Shortly after making their model publicly available, the research team was inundated with requests from Spain to Silicon Valley. Biopharmaceutical companies, government entities, and fellow academics were interested in applying the model to their own work.
Over the summer, Barbastathis and Dandekar began collaborating with Rackauckas and Emma Wang, a sophomore studying electrical engineering and computer science, to make their model even more useful to other researchers across the world. The result is a toolkit that offers both diagnostic and predictive data on a more granular level.
“With our new model, we are able to transform data about Covid-19 into data about how well quarantine measures succeeded in containing the spread per country, and even per state,” says Rackauckas. “Now we have a tool that can assign a global quarantine strength score that researchers can then use to correlate to all sorts of other social phenomenon.”
According to Barbastathis, the resulting model is a testament to what can be accomplished through interdisciplinary collaboration. “Our team represents four different departments and we’re very proud of that,” he says.
The team hopes that the new model will provide insights into exactly which quarantine or social distancing methods are most effective in stopping the spread of the virus. “Our aspiration is that our model can actually correlate the rate of this growth with various aspects of the policies that are being followed,” Barbastathis adds.
While Barbastathis and his colleagues are hoping to understand the spread of the virus on a national or state level, Lydia Bourouiba, associate professor of civil and environmental engineering with a joint appointment in mechanical engineering at MIT, is trying to understand the spread on a micro level.
Mapping the path of viral particles
Bourouiba has spent her entire career trying to understand how diseases spread from one person to another. After her experience as a graduate student in Canada during the outbreak of SARS-CoV-1, commonly known as SARS, she combined her expertise in fluid dynamics with epidemiology, studying the transmission of a range of influenza viruses as a postdoc and instructor.
When she founded The Fluid Dynamics of Disease Transmission Laboratory at MIT, Bourouiba continued to focus on fundamental fluid dynamics in relation to pathogen transmission, as well as how droplets are exhaled from one person — through sneezing, coughing, or breathing — and spread through the air to another person. This research combines experiments and modeling.
Early this year, Bourouiba became concerned about the patterns she was noticing with the virus that would soon be named SARS-CoV-2, or Covid-19. “I was paying very close attention to the unprecedented efforts of control that were deployed in Wuhan. By the end of January, it was very clear to me that this was going to be a pandemic,” recalls Bourouiba.
She started sounding the alarm to various agencies and organizations while continuing to pursue ongoing efforts in her team’s research. She also focused her teaching in course 2.250 (Fluids and Diseases) on events related to SARS-CoV-2.
In late March, Bourouiba published research in JAMA that continued to discuss the paradigm of disease transmission she had proposed in the past, including during a TEDMED lecture in 2019. In the article, she made a call to challenge and update the current scientific framework that has shaped public health recommendations about the routes of respiratory disease transmission.
Many government and health organizations had used a disease transmission framework developed in the 1930s by William Firth Wells to inform mask policies or social distancing rules, such as staying six feet apart from others. However, based on years of research, Bourouiba found particles exhaled from an individual can travel much farther than previously thought.
The main problem with the outdated model is how exhalations are classified. “The physics of the process of exhalations cannot be categorized into isolated large droplets verses aerosols,” says Bourouiba. “It’s a continuum of droplets moving within a multiphase gaseous cloud, and the cloud is critical to drive the overall flow.”
Bourouiba’s team uses a combination of modeling and optical techniques including high-speed imaging, shadowgraphy, schlieren, and a range of particle detection and imaging, to map the transient flow of various exhalations. They use these technologies to image and quantify a range of exhalations — including coughing and sneezing — and create models of these complex flow exhalations. The resulting gaseous cloud can carry and propel droplets expelled up to 16 feet away from a cough and up to 27 feet away from a sneeze.
The findings and public awareness in Bourouiba’s article helped reshape guidance on wearing face masks in public in various locations. Many, including Bourouiba, felt the substantial delay in issuing guidelines on face masks in some locations did not help with desirable early critical containment of the epidemic.
“The review of the SARS event and the toll it had — although now dwarfed by SARS-CoV-2 — led to one major lesson learned: We cannot wait to have definitive and final scientific answers in the heat of a pandemic, typically involving a new pathogen. The precautionary principle should always be used in combination with continuously evolving knowledge,” she says “In addition, investments in research on prevention and control between pandemics is as critical to allow a strong basis of knowledge to start from in these regularly occurring local or global events.”
Moving forward, Bourouiba will focus on studies that build upon her previous work. This will include multiscale fluid modeling pertaining to the assessment of material efficacy for respiratory protection and collaborations to examine the fluid dynamics effects of the actual Covid-19 virus and other pathogens. She is also focusing on air flow in indoor settings, in particular in educational or health care-related settings, to ensure the safety of occupants, patients, and health care workers.
Another team at MIT has also been focusing on the safety of doctors, nurses, and front-line workers through the mass production of a disposable face shield. Martin Culpepper, Class of 1960 Fellow and professor of mechanical engineering, and his team at MIT Project Manus were one of the first groups of researchers to ramp up manufacturing of a final product in an effort to protect people from the spread of Covid-19.
Protecting essential workers
With the number of infected individuals rising rapidly in cities like New York and Boston, Massachusetts in March, a primary concern in the fight against Covid-19 centered on personal protective equipment, or PPE. N95 masks and other protective equipment were in short supply. Many health care professionals were advised to keep masks on for longer than what is safe, putting both themselves and their patients at risk. Labs across MIT donated masks and gloves to local hospitals to help address the shortage. Meanwhile, well-intentioned people turned to sewing machines and 3D printers to make non-medical-grade solutions.
Culpepper worked with Elazer Edelman, the Edward J. Poitras Professor in Medical Engineering and Science at MIT, director of MIT’s Institute for Medical Engineering and Science, and head the MIT Medical Crisis Outreach Team, to tackle this problem. In addition to being a professor at MIT, Edelman is a practicing cardiologist at Brigham and Women’s Hospital. The pair took a different approach to tackling the PPE shortage.
“People were trying to deal with the mask shortage by making more of them, but we wanted to slow down the rate at which health-care workers need to change their masks,” Culpepper explains.
The solution they landed on was a low-cost disposable face shield that health-care workers could secure around their face and neck — protecting themselves and extending the use of the mask they wore underneath the shield.
Culpepper began working on the initial prototype of the face shield at home in early March. With the help of a laser cutter in his basement and the assistance of his children, he tested materials and made a few prototypes. MIT Project Manus staff then made dozens of the prototypes using a laser cutter in the Metropolis makerspace to iterate the design to a final state. They also used a Zund large-format machine in MIT’s Center for Bits and Atoms to experiment with materials that can’t be processed on a laser cutter. Culpepper collaborated closely with Edelman to test designs in the field.
Edelman worked with his colleagues at the hospital to get feedback on the initial design. “I brought the prototypes into the hospital and showed nurses and physicians how to store, assemble, and use these devices,” says Edelman. “We then asked the nurses and physicians to use them in non-Covid situations to give us feedback on the design.”
Culpepper notes that Edelman’s perspective was vital to the project. “Elazer has ‘mens et manus’ in his veins,” says Culpepper. “He has an amazing way of taking clinician feedback, combining it with his experience and perspective, and then translating this all into actionable engineering speak. He was a critical link in the chain of successes that made this happen.”
Armed with positive feedback from clinicians, Culpepper and MIT Project Manus looked to mass produce the shields. The shields were specifically designed to be manufactured at scale. Die cutting machines could easily cut the design into thousands of flat sheets per hour. The sheets were made of polycarbonate and polyethylene terephthalate glycol, materials carefully chosen to ensure there wouldn’t be strain on the supply chain.
MIT and the face shield manufacturer, Polymershapes, donated over 100,000 face shields to hospitals, urgent care centers, and first responders in the areas hit hardest by the virus, including Boston and New York. As of October, over 800,000 shields had been produced by Polymershapes.
According to Culpepper, the supply chain stabilized more rapidly than had initially been predicted. “I’m happy the supply chain for face shields is righting itself. It was our job to be the stopgap, to be there when people in an emergency needed something quickly until the supply chain stabilized,” he reflects.
The face shields have helped protect hundreds of thousands of health-care workers and patients who otherwise would have needed to turn to unsafe PPE options as cases rose exponentially.
Over the summer, signs of life slowly returned to campus. More research teams were allowed to go back to their laboratories to resume work on non-Covid related research. A number of undergraduate seniors moved on campus to take classes with in-person components. While many mechanical engineering groups can shift their focus back to other research projects, developing solutions for the new reality the world faces will continue to be a priority.
“We have an obligation to use our diverse set of skills and expertise to help solve the pressing problems we now face in light of the pandemic,” says Wang.
Until a vaccine is administered to enough people to stop the virus in its tracks, mechanical engineers will continue to collaborate with researchers and experts across all disciplines to develop technologies, products, and research that deepens our understanding of the virus and aims to slow its spread across the globe.