Tell us a little about the work you do, and why it is important to you.
I’m a second-year MPH student studying hospital and molecular epidemiology. My main research focus is antibiotic resistant hospital acquired infections, although I’ve also done some work with vaccine surveillance and lead emissions from residential demolitions.
My work is important to me for two reasons. Firstly, I’m intrigued by the narrative we have around antibiotic use. We talk about “conserving antibiotics” and financial incentives to encourage new drug development, but we rarely discuss actual eradication of antibiotic resistant organisms. I was lucky enough to find a lab that’s doing prospective surveillance of antibiotic resistance –trying to predict what’s going to evolve and under what conditions – and I think that’s one of the first steps in asking the questions even more preventative than finding new drugs: how do we stop needing new drugs?
Secondly, my academic program combines molecular biology with epidemiological studies. In many instances, this lets us do more with data: we can connect molecular markers to clinical relevance, and look at epidemiological data with a much finer lens. Molecular tools are rapidly becoming invaluable for both infectious and chronic disease.
What programming languages or tools do you use, and how do they fit into your work now?
My primary tool is SAS, which I learned as part of my graduate school coursework. I’ve started using MATLAB – also taught through coursework – for mathematical modeling. This year I took a Software Carpentry course, which aims to teach coding for the express use of researchers. That introduced me to R, as well as tools like GitHub.
As I’ve progressed through my education, I’ve come to see, more and more, the value in knowing an actual programming language. There are a number of tools I’d like to build, but I’m limited by only knowing statistical packages. Many in my cohort have expressed similar frustration: some of the work we want to do would have to be outsourced to a computer scientist. But I don’t see this divide is necessary: there are languages that would allow us to both build tools and conduct statistical analysis. If we have to learn a statistical package anyways, why not spend that time learning a tool that gives us even greater flexibility?
Luckily, there are so many classes and resources online that it’s easy to begin to teach yourself to code. The greatest motivation for being able to learn to code is having a goal or project you want to build yourself. I have the motivation to teach myself independently, and the tools to do so exist, so I’m optimistic! And as the next generation of epidemiologists see the value in these tools and find ways to learn them, I think it will work itself naturally into the curriculum of epidemiology.
I noticed you have a background in the humanities. What path did you take to get where you are now, and what role does that training play in your current work?
As an undergraduate, I dual majored in biology and philosophy; although I spent the summer working in laboratories, during the academic year I spent the vast majority of my free time working as the arts editor for our college newspaper.
These experiences turned out to be invaluable for studying epidemiology. Philosophy is a study of analytical thinking. What is the minimum standard of evidence that we need to make this assertion? Can we really say that A causes B? How I use inductive versus deductive thinking? What piece of information would prove or disprove my argument? Philosophy trained to think me like an epidemiologist, even if I didn’t yet have an epidemiologist’s vocabulary.
Journalism was just as helpful. When you’re conducting an interview, there’s no guarantee that your questions will elicit the information you want, or even that you want the right information! There were so many times I started writing a story about one subject that morphed into something completely different when I started interviewing. We see this challenge in epidemiology all the time: what if there are factors at work that we don’t even know to measure? How do we design surveys and gather information in such a way that gives us the data we’re actually seeking? Epidemiology and journalism are both storytelling: using data from real-world observations to craft a narrative that explains a cause.
Transitioning to epidemiology wasn’t that hard, as I’d always been involved in science. But I’ve always been glad some of my background was in the humanities as well.
I love what you've said in the past about epidemiology being a set of tools and not a set of facts. What can epidemiologists or students can do to prepare for that trajectory?
I came to graduate school having memorized a lot of information about diseases and historical outbreaks – that is, having learned a set of facts. Upon beginning my degree, I rapidly realized that epidemiology is about equipping us with a set of tools that we can use to go about acquiring those facts. I wasn’t the only one under this illusion: after the first semester, many of my peers switched from the epidemiology track to health behavior/health education.
I love epidemiology, but that’s because I now have the tools to answer my own questions about disease, rather than being limited by other people’s discoveries. However, if I’d had a better understanding of epidemiology from the beginning, I would have prepared differently. I would have taken a lot more coursework in statistics and computer science; I would have focused on developing skills in data management and analytical thinking (so I’d do the philosophy degree again!)
With epidemiology, it doesn’t matter what you’re interested in: you can use tools to study chronic disease, infectious disease, environmental contaminants, the impact of preventative health measures, anything. If you’re trying to prepare for a track in epidemiology, read plenty of books on public health– the type of content that will remind you what questions you may be interested in – but focus your classwork on developing your analytical thinking and quantitative skills.
What do you see as the future of epidemiology? What are your hopes for public health in the next 10 years? How does coding fit into that future?
I see the field becoming even more interdisciplinary. With the massive amounts of data becoming available, it’s going to be even easier to look at an outbreak in the context of so many things: economic factors, environmental factors, anthropological factors. We can think about disease in the context of greater networks. That probably wouldn’t be possible if we as epidemiologists weren’t getting better and better tools, but I think that’s where coding comes in: we can build the types of things we need to think about and interact with data in different ways.
This openness of data also means more people will be able to contribute to epidemiology: it’s not just going to be people with advanced degrees debating science in the slow journal publication cycle. Now, as tools and information are widely shared, anyone with initiative will be able to contribute to epidemiological questions.
In the next ten years, I hope epidemiology becomes more predictive. Right now, despite falling under the field of public health, things often still feel very responsive: we responded to the recent Ebola outbreak, we’re responding to Zika, we’re responding to the lead in the water in Flint, MI. But maybe we can start building tools to get past that problem. What if someone had decided to build a platform that integrated civil engineering data with health department data and overlaid it all on an interactive map? Maybe we could have anticipated a problem before it began. I hope that’s what coding can start helping our field to do.
Epidemiologists changing the future of public health.