September 21, 2022
September 19 to 25 marks Science Literacy Week in Canada, an opportunity to showcase how science, discovery and ingenuity shape our lives. The theme for Science Literacy Week 2022 is mathematics so we decided to sit down with two young scientists to talk about mathematical modelling of infectious diseases and their own personal work in that area.
Alison Simmons is a fourth year PhD student in the Dalla Lana School of Public Health at the University of Toronto. She is co-supervised by David Fisman and Ashleigh Tuite, both professors in the division of epidemiology at the Dalla Lana School of Public Health.
Jesse Knight is in the fourth and final year of his PhD in the Institute of Medical Sciences at U of T. He is supervised by Sharmistha Mishra, an associate professor in the Temerty Faculty of Medicine at U of T and a clinician scientist at St. Michaels Hospital, a site of Unity Health Toronto.
Can you tell us about yourself?
Alison Simmons: I’m from a small town in western Pennsylvania and did my undergraduate degree at Bates College, a small liberal arts school in Maine. Then I completed my masters at Brown University in Rhode Island and now I’m doing my PhD here at the Dalla Lana School of Public Health at the University of Toronto.
Jesse Knight: I was born in Brampton and I went to the University of Guelph for my undergrad in biomedical engineering. I also did my masters at Guelph on the topic of medical image processing, specifically brain MRI segmentation.
What drew you to math and specifically, mathematical modelling in infectious diseases?
AS: I love math. It was my favorite subject from elementary school onwards. In high school, I found that I was interested in biology so when I entered university, I was trying to think of ways to bring those two interests together. I ended up majoring in biology and doing a concentration in math and a concentration in public health. I took classic math classes like calculus but I also took a course that was called mathematical models in biology, and that’s where we learned how math could be applied to biology to model biological systems.
JK: I’ve always been interested in math. Both my parents are high school math teachers, and they will tell a story how as a child, I would sit under the kitchen table after dinner and ask them to ask me math questions. I had never really heard about the concept of mathematical modeling of infectious disease until I applied for a job here at the MAP Centre for Urban Health Solutions. After reading the job description, I thought, I’d never heard of this field but it sounds so interesting! It was really cool to use all the equations I learned in class, and I came to be fascinated by how each parameter – a number input to the model – can have a dramatic impact on the model outputs. I was in love since before I even got the job.
Can you tell us about your research?
JK: The goal of my thesis is to explore how the answers that we get from math models of HIV transmission change depending on assumptions that are intrinsic to the model. Most of the time, it’s fine to reuse the same equations and assumptions that have been used before, but there are certain situations where we want to think more critically about whether the equation or assumption is appropriate in the context of the current research question. A classic example is how sexual partnerships are simulated. The usual approach essentially adds up all of the potential risk over the course of a partnership and defines an average transmission rate for each partnership type. But this approach can overestimate transmission in long-term partnerships, which then influences how the model predicts resources should be prioritized to various risk groups.
AS: For my PhD, I’m using mathematical modelling to look at what is the most cost-effective vaccine to give to children to prevent invasive pneumococcal disease in the larger population. What I’m actually modeling is transmission of the bacteria Streptococcus pneumoniae, a common cause of meningitis and pneumonia. A lot of children carry this bacteria asymptomatically in their nose. But for other folks, like older adults or people who are immunocompromised, it can lead to an opportunistic infection with severe disease outcomes. When the pneumococcal vaccine was introduced for kids, there was a big drop in invasive pneumococcal disease among older adults because little kids were infecting their grandparents. I’m trying to model how immunizing kids impacts invasive pneumococcal disease across the entire population and which vaccine is the most cost-effective.
What is your day-to-day like as a mathematical modeller?
JK: It’s about half coding and half writing. The coding aspect involves things like implementing the model equations, running what we call “sanity checks”, to see that the model does what we expect for certain model inputs, analyzing data to define all the model inputs, designing scenarios and experiments, and then making some nice graphics to summarize the results. With respect to writing, I would include in that reading and writing papers.
AS: I’m still developing my model so most of my day today is reading articles and adapting code from previous models. I’m also thinking through how to use the public data that’s available to answer the questions I want to answer.
What real-world impacts has your work had or do you hope to have?
AS: Invasive pneumococcal disease is something that could largely be prevented yet there is still a large burden on our population. Merck and Pfizer will both be rolling out new pneumococcal vaccines soon so knowing which vaccine is the most cost-effective is a pressing question. I hope that my model can be used by governments and the National Advisory Community on Immunizations to make evidence-based decisions on which vaccine to recommend.
JK: All of the modeling around HIV transmission that I’ve done has been contextualized in southern Africa, specifically a country called Eswatini. One study I worked on showed that the rate at which people enter and leave certain risk groups can be a large determinant of how resources should be prioritized. We often use risk groups in these transmission models, but we are not necessarily thinking about people entering into that risk group, like entering into sex work and then leaving sex work. Our study showed that this is an important dynamic to capture, considering how large differences in risk can emerge over a relatively short period. This paper caught the attention of people at UNAIDS, who recognized that this parameter should be included in future models going forward.
What is one thing that people often misunderstand about math modelling that you’d like to set the record straight on?
AS: Math modelling is really a simplification of complex interactions. Human behaviour is really hard to capture with math equations because humans are complex! They change their behaviour and respond to things differently. It’s important to think about math modelling as a tool that can provide extra evidence and help inform decisions, but there’s always going to be limitations and assumptions you have to make in a model. It’s a trade-off between how complex and detailed your model is and how much information it provides and how soon you can generate that information.
What do you want to do after your PhD?
JK: In the short term, I’m thinking about doing a postdoctoral fellowship because my long-term plan is to be a faculty at a university or a scientist in an affiliate center. With the postdoc, I’m looking to grow my professional network and meet other modellers with different perspectives, and learn about different types of models and diseases to really round out my areas of expertise.
AS: I’d love to work for the federal or provincial government as an in-house modeller that can inform their health decision making. It’s nice to have a skill set like math modelling where I can easily pivot to address different questions about emerging or re-emerging infectious diseases.
Math is usually not a well-liked subject in school. What is your elevator pitch to convince people to give math a chance?
JK: Our brain and our whole reward system is built around predicting things. At the core of our intelligence is our ability to form models in our head about how things work and how things will happen given some things we already know. Math is the core tool that allows us to build those models, whether its simple ones in our head, or fancy complicated ones that run on supercomputers to answer interesting questions about everything from infectious diseases to climate change. If you want to be able to answer interesting questions and learn about the world, math is at the core of all that. It allows you to move beyond knowledge and get into predicting things, which is really powerful.
What would you say to a high school or middle school student who is interested in math and maybe math modelling?
AS: If you really like math, take a lot of math courses, but also explore other areas in school. You don’t always have the opportunity to learn from experts in different areas so I think it’s a missed opportunity if you don’t explore different subjects. For example, I took a lot of sociology courses as an undergrad and that’s really helped me develop a more holistic perspective on public health in my career path. Another piece of advice is to look at job postings that you find really interesting to figure out what skills you need to help you get there.
JK: In the modern era, applied math and modeling go hand-in-hand with code. I would say learning to code is an essential skill today. With code you do everything from building a website or a small video game, to predicting election results with machine learning. You’ll end up using math to solve real problems, and probably have fun while doing it! I really wish code was taught more regularly in high school these days because I think everyone could benefit from that. Fortunately, there are tons of great resources online, especially for learning something like Python.