Interview with Eric Bakota: "Future generations of epidemiologists will need to focus on the science of data"
I'm excited to kick off a new series of interviews with epidemiologists in public health practice, academia, and industry. The inaugural interview is with Eric Bakota, an epi with the Houston Health Department. I met Eric at an event called EpiHack Analytics, which pretty much sums up our shared interest in bringing data science to epidemiology.
I am currently focused on three projects: a epi-surveillance tool in R Shiny to allow for data exploration of communicable disease cases; a QI/QA report using R Markdown to inform managers of the surveillance team's efforts; and the 3rd edition of Houston's Epi-in-Review, which is a compendium of analyses for each reportable condition. The graphs for the book were made using the "ggplot2" package. I love that these projects draw from the same database yet serve very different functions by informing different audiences (epidemiologists, managers, and the public at large, respectively).
What challenges and opportunities have you faced working as a tech person in a more traditional public health department?
Do you have any advice or words of encouragement for people looking to get started with coding?
I think the best way to learn to code is to find an interesting problem that you're motivated to solve -something you're willing to work on from home.
What do you see as the future of epidemiology?
Coding for analysis and coding for automation are, I believe, the future of epidemiology. On some fronts it is already happening. The RCKMS project led by CSTE & CDC is a great example: the projects end goal is to have reportable condition case reports be automatically generated using electronic medical records and electronic lab reports. This will allow surveillance investigation epidemiologists to focus on analysis of data instead of acquisition of data.
Future epidemiologists will need to be less focused on understanding the natural history of disease, its transmission, etc and more focused on understanding the science of data, which includes regression techniques, machine learning, and the grammar of graphics.
Epidemiologists changing the future of public health.