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CU Boulder researcher receives NSF funding to study COVID-19 spread in airplane cabins

At least two companies, AstraZeneca and Moderna, are preparing to enter 鈥減hase three鈥� human trials of a vaccine for the SARS-CoV-2 virus this summer, with other companies not far behind, Soumya Swaminathan, chief scientist for the World Health Organization, on June 26.听 听

But even if everything goes right, the U.S. Department of Health and Human Services doesn鈥檛 expect to have enough doses available for the American population until at least January 2021.听

In the meantime, people will continue to be exposed, perhaps especially those who travel by air.

鈥淯ntil we have a vaccine or a cure, this virus is not going to go away. But at some point, all of us are going to have to start traveling,鈥� says Maziar Raissi, assistant professor of applied mathematics at the University of Colorado Boulder, a specialist in machine-learning and deep-learning algorithms.听

With that conundrum in mind, Raissi submitted a research proposal to the Division of Mathematical Science at the National Science Foundation to use 鈥渃omputational mathematics and state-of-the-art physics-informed deep learning techniques鈥� to model, analyze and predict how air flow in an aircraft cabin will influence contagion.听

鈥淎s we know, this virus is airborne,鈥� he says. 鈥淪tudying how it travels in the circulating air flow inside an airplane is very important.鈥澨�

Raissi, who joined the CU Boulder faculty in January, pulled his initial proposal together in just four days this spring. On June 4, the NSF notified him that the project had been funded.

鈥淚 have not seen this kind of turn around ever in my career,鈥� says Keith Julien, chair of the Department of Applied Mathematics.

Because of the high cost and difficulty of studying air flow in an actual airplane, Raissi will use physics-based models to mathematically describe how air moves inside the 鈥渃omplicated geometry of an airplane cabin.鈥�

The research will 鈥渆fficiently combine鈥� physics-based models for 鈥渇luid dynamics, scalar transport, epidemiology and airborne infection to analyze the spread of COVID-19 within a closed system such as an airplane,鈥� he wrote in his proposal.听

鈥淲e鈥檒l analyze the data and use it for prediction of what will happen if somebody coughs next to us, for example,鈥� Raissi says. 鈥淚s it going to be filtered out or not? Will it make a difference if the (coughing) person is sitting next to us, or five rows in front of us?鈥�

Raissi says the data will be useful in determining aspects like how to seat passengers, passenger density, and the rate of flow from aircraft ventilation systems. For example, he says, the data might indicate how to better direct air flow to reduce the risk of infection.

He plans to produce open-source software that is 鈥渁gnostic to geometry,鈥� he says. In other words, by plugging in different variables, the software should be able to predict how air flow affects the virus in myriad different spaces, from supermarkets to college campuses to trains.听

Raissi came to CU Boulder after working as a senior software engineer with the Silicon Valley firm . Prior to that, he received his PhD from the University of Maryland College Park and carried out postdoctoral research at Brown University.

鈥淚 received the offer from CU Boulder before getting the offer from NVIDIA,鈥� he says, but deferred coming to Boulder for a semester in order to gain experience in private industry.

The field of machine-learning went into hyperdrive following the 2012 publication of work on artificial neural networks by British-Canadian computer scientist and cognitive psychologist Geoffrey Hinton, known as the 鈥済odfather of deep learning.鈥�.听

鈥淭hat has led to a lot of cool innovations,鈥� Raissi says.听

鈥淧hysics-informed deep learning is the idea that you replace data with physics, the laws and equations discovered by Newton, Einstein and other super geniuses. 鈥� This means the machine doesn鈥檛 have to learn everything from scratch. Rather, we can transfer human knowledge gathered through centuries of scientific discovery to a deep neural network.鈥�

With the continuing proliferation of self-driving cars, drones, robots and other artificial-intelligence machines, it has become critical to ensure that deep neural networks will be safe, secure and essentially unhackable. As machines become more and more autonomous, it may even become necessary to program a kind of 鈥渕orality鈥� into their learning and decision-making. For example, a self-driving car may have to decide whether to hit a person who has jumped into the street or risk an accident that could injure or kill a passenger.

鈥淲e want to create a balance between maximizing reward or minimizing the loss. But at the same time, we have to be morally correct,鈥� Raissi says, describing such situations as 鈥渁 multi-objective optimization problem.鈥澨�

鈥淗ow moral do we want to be, versus how much we maximize profits鈥攅ven humans have difficulty with such choices.鈥�

With COVID-19, Raissi says that, 鈥渨e have to carefully balance the tradeoff between saving people鈥檚 lives and their livelihoods.鈥�