Dionne Aleman, PhD, P.Eng. is using Industrial Engineering Techniques to Track COVID-19’s Spread

Daily during the Provincial and Federal government briefings we are given new measures, policies, and details on how to best “flatten the curve” to stop the rapid spread of COVID-19. As the virus spreads on an unprecedented scale, and new data comes in, we begin to understand the efficiency of policies like social distancing. As the government tries to control the virus, new data can help us understand which public policies are most efficient and which different practices best combat the spread.

Dionne Aleman, PhD, P.Eng., industrial engineer and professor in the Department of Mechanical & Industrial Engineering at the University of Toronto (UofT), is hoping that her new research in pandemic modeling can lead to decisions that curb the spread of COVID-19.

aleman 2
Photo courtesy of the University of Toronto

It’s easy to see that Dionne Aleman was always destined for industrial engineering. She was born into a family of them—even if she didn’t know it. After talking to her high school guidance counsellor, she landed on industrial engineering as a dream career, largely because of its ability to meld her passions for English, Math, and Sciences. “I went home to tell my parents I know what I want to be I want to be an industrial engineer and they both looked at me like I was crazy and said ‘We’re both Industrial engineers, how did you not know that?’”

As Aleman explains, it’s an easy mistake to make. “Most people who are industrial engineers don’t have a literal job title of industrial engineer. It’s almost like a feeder degree of a wide variety of areas where just being technically minded is a huge advantage.” Aleman’s mother spent her time running the family business, and her father worked for the Miami-Dade transit authority, but both were very much involved in efficiencies and processes.

Industrial engineering’s ability to blend different fields is what makes Aleman’s work in her Medical Operations Research lab at UofT so valuable during the COVID-19 global pandemic. Her and her research team’s pandemic modeling is based on applying operational research techniques to medical decision making. The hope is that by applying methodology from operational research, which is a branch of industrial engineering, to medical policies, the most vulnerable members of society can be protected in times of crisis, such as the spread of COVID-19. This is made possible because Aleman’s research is focused on providing specific, data-based evidence on which groups are most vulnerable to a virus like COVID-19. By identifying these groups, the government can adopt public policies that are targeted to protect and benefit them, and consequently keep the general public safe.

By bringing the strength of her industrial engineering background to medical policies, Aleman hopes to find deeper meaning from traditional epidemiological models that rely on reproduction numbers. “A reproduction number is the number of people that an infected person will infect on average,” she explained. “So for COVID-19 it’s thought to be around 2.3. One infected individual turns into two infected individuals and that’s how we get that exponential spread.” While the simplicity of the R0 number allows it to be applied broadly, it comes with its own set of flaws. “While it might describe the trajectory of a pandemic in a city or worldwide pretty well, it doesn’t necessarily help to understand what is the impact of individual polices like telling people to socially distance themselves, closing schools and universities, closing down workplaces, because all of these different ways that people have contact with each other.”

Rather than broad based data collection, Aleman and her team are interested in how specific demographic data sets interact with each other. Not only the number of citizens in a city, but who lives in what neighborhood? Which transit systems do they take? Which routes of that transit system are they taking? By collecting as much data as possible, and making it intersect in their pandemic modelling system, Aleman can retrieve precise information on how a virus might spread.

“The reproduction number in each of those situations should be different. Just thinking of kids: they’re germy and have no sense of hygiene or personal space, so germs spread like wildfire amongst children. One would think that the R0for any disease would be much higher within a school than say a restaurant or an office. People have very different types of contact between each other.”

Aleman acknowledges the strength of R0 in showing epidemiological curves for quick mapping, because it requires limited data. However, with that speed comes a lack of workable information. “We wanted to know if we had something that really looked at person to person contact and possibility of person to person transmission where any two people might have a different rate of transmitting or receiving the disease.”

This optimization strategy is at the heart of industrial engineering and its ability to find efficient solutions that are applicable to policy. Because their data is specific and could pinpoint crucial factors, it can lead to government policy decisions that target the right groups. As Aleman says, “Rather than just saying elderly people are much more impacted…maybe it’s people of a particular age group, who live in a particular neighborhood who take a particular transit route.”

Unfortunately, by applying industrial engineering to medical decision making, Aleman experiences difficulties within these medical community. As with any innovation, there can sometimes be resistance to change. While COVID-19 sheds a glaring light on the importance of this kind of cross-pollinated thinking, Aleman reports that not everyone understands the interdisciplinary process,  “Ultimately it is difficult to get research funding for this type of work because the techniques that are involved are not necessarily novel from an engineering perspective. From a medical side, it’s sort of a vast departure from what is traditionally used by health care officials. It kind of ends up that this work ends up in a neither here nor there area.” The innovation in Aleman’s work is that it takes operational research techniques of industrial engineering and applies them to a new field—medical decision making. While she is repurposing traditional techniques, the field of medical knowledge is not used to this, leading to this odd status where neither group feels totally comfortable claiming her research, and therefore, funding her research.

In addition to the interdisciplinary funding struggle, there is also the difficulty caused by trying to combine different priorities. “Epidemiologists tend to be very comfortable with the methods that they have […] I think part of that is more so thinking about how the disease is moving on a very high level. They’re not necessarily thinking about the nuance of public policy.” While epidemiologists are thinking on a high level, Aleman is hoping that the deeper analysis provided by her pandemic modeling can provide specific evidence to help make public policy changes aimed to stopping a pandemic.

Despite the hesitance in some areas, Aleman and her team continue to do pioneering work that thinks outside the box and can lead us out of crisis like COVID-19. It’s important to remember in unstable times of the wide impact engineers can have on society. Aleman stands as an excellent example of leadership in engineering, and how rigorous, evidence-based thinking can lead to wise policy decision making.

For more on COVID-19 and its affect on engineering in Ontario, visit our COVID-19 news page

Leave a Reply