Today's interview is with Dr. Bryan Lewis, who was my PhD advisor at Virginia Tech! Bryan is the reason I learned to code (in Python!), which ultimately convinced me to switch from liberal arts to computational epidemiology. I thought if he convinced me, he should probably be able to convince you.
1. How did you come to be a computational epidemiologist? It's not a very common field!
Because its awesome! Fighting infectious disease with computers, what could be better? Seriously though, I was fortunate to have the opportunity to do research while I was getting my MPH with some very gifted infectious disease modelers (Joe Eisenberg and Travis Porco). I took a couple of classes and worked on a couple papers with them, and enjoyed it greatly. Travis was a great mentor and spent a lot of time helping me work on my master’s thesis, which was a small modeling project. After working on that and spending a couple years with Joe doing research with them I was pretty hooked on the field, and increasingly became convinced that it was a growth area that I could best make a contribution in.
2. Before coming to Virginia Tech you worked as a TB epidemiologist at the California Department of Health. How did your background in computer science fit in with that role?
It was a pretty good fit, since traditional PH departments need good epidemiologists, which requires good data analysis. As a person who could actually write some code and think more “computationally” I was able to help on projects where the more “classically” trained epis couldn’t analyze the data in a particular way. I recall “realizing” this at some point while I was still just an intern, when I got a task to analyze just a couple questions of a survey from a senior epidemiologist in the TB epi department. Once I understood what she wanted with that batch of questions I asked about the rest of the survey and she leveled with me, “I don’t know how convert those data into the format I need for the analysis I want to do. The rest is easy.” To me analyzing and interpreting the whole survey was the daunting part, converting the data with a small program was the easy part. So it was a good fit, I learned a lot, and think that as a “coding-oriented” person I was able to help a lot. There were multiple projects where that was my role, and the more senior classically trained epis did the interpretation and “standard” set of analyses.
3. What competencies do you encourage in your students? What challenges do they see them face when acquiring those skills?
Data analysis is crucial in so many disciplines, its hard to go wrong. So if you are talking about classes, I’d steer folks towards stats, programming of some kind (partial to Python), and a quantitative science class or two to in a field of interest to apply these skills to. For many students it seems there is an obstacle to tackling some of these classes because the material can be dry when not in the context of a juicy problem. I think programs that can incorporate a “find a problem based project” phase or a general “project class” that can act as the test bed for applying some of techniques can be quite instructive. After you’ve been confronted with a problem you’ve wanted to solve and then faced a limitation due to not having a technique in your toolbox, your hunger for more techniques will grow. Then when faced with the dry presentation of a technique a deeper understanding can occur. This problem based learning is, in my opinion, the best way for students to learn and at their heart what most PhD programs try to foster.
4. What do you see as the future of epidemiology? How does coding fit into that future?
I think coding and more computational techniques are going to play a huge role in the future of epidemiology. At its core epidemiology is about collecting and analyzing data about disease, computational techniques can make this both more efficient but also more complete. Historically the data available for analysis has been constrained by the labor of collecting it. As we enter a world where more and more non-traditional data are available, coding will be essential for gathering, organizing, and analyzing it. I absolutely think there will a lot that “digital epidemiology” can contribute to the field, and coding skills along with a keen understanding of epidemiology and appropriate techniques for analysis will be essential.
5. These days you do a lot modeling. What is the role of modeling in public health, and what role do you hope it plays in the future?
Again, I am very heartened by the trends I’ve seen in the last 15 years I’ve been “in” the field. I certainly don’t think modeling will supplant traditional epidemiological analyses. However, modeling methods are becoming more common place and are more embraced by the public health community every year. The variety of studies I see now where some form of mechanistic (rather than statistical) modeling has been applied has grown significantly. I think of it as an increasingly useful tool. Much like surveys and logistic regression now, I think in the future mechanistic models will add significantly to our understanding of diseases, and offer insight to ameliorating their effects.
Epidemiologists changing the future of public health.