Episode 2: Battling Infectious Diseases with Dr. Zoe McLaren

Amy Barnes14min

Dr. Ian Anson

00:05

Hello and welcome to the CS three podcast at UMBC, a production of the Center for Social Science scholarship. I'm Ian Anson and on today's show, as always, we'll be hearing from UMBC faculty, students, and community partners about the social science research they've been performing in recent times. Quantitative,qualitative, applied empirical, normative. On the CS3 podcast we bring the best of UMBC social science research to you.

Dr. Ian Anson

00:36

On this week's episode, we talk to Zoe McLaren, Associate Professor in the UMBC School of Public Policy. Professor McLaren has done a great deal of work in recent years on health epidemics such as HIV and tuberculosis, especially in Africa. What's more, Professor McLaren is an affiliate of the Health Econometrics and Data Group at York University in Toronto. Professor McLaren's expertise in health economics has led her to recently tackle a new problem, the novel Coronavirus. It should be obvious at this point that more COVID-19 research is desperately needed. As epidemiologists, birologists, and infectious disease experts work in labs around the clock to produce things like safe, effective vaccines that can handle all the variants of COVID that have sprung up around the world. Laboratory researchers today are putting their pipettes and their petri dishes into overdrive, and their efforts are saving lives. But what about the social sciences? On today's episode, we discuss how social science research, when applied properly, can be vital to our efforts to battle communicable diseases like COVID. Professor McLaren's passion for this work is evident in our interview, and I hope that her words can inspire all of you who are listening to keep advancing your own social science research enterprise.

Dr. Ian Anson

01:51

Zoe McLaren, thank you so much for agreeing to talk with us today. I'm really grateful for your time, of course. And I really am excited to hear about some of these insights that you have about public health, about your research on COVID that you've been working on recently, but also some other topics, obviously, that you've been studying over the long haul in several sort of aspects of your career. So the first thing I wanted to ask you really was to maybe talk a bit about this recent work that you've been doing on vaccinations and COVID-19. And of course, since this is a podcast that's focused on the social sciences, and on the sort of process of social sciences, I wanted to know what kinds of data or analytical tools or approaches have you used in the past to make the kinds of recommendations that you do in this recent research, that maybe even in some of your other research, sort of your overall approach to empirical social science.

Dr. Zoe McLaren

02:38

Thanks Ian. It's a pleasure to be here. So my work is overall very data focused. So the idea behind the work that I do is that we have these important, often life or death policy questions that we want to answer. And then we have to see what kind of data is available. And so some of my work in the past has been about trying to develop connections that would get me access to data that hasn't been more widely released or isn't publicly available, to widen the range of data that we have to answer these questions. And then once we have kind of know what the data available is, and we know our policy question, the gap in between is the statistics that will help us analyze the data, develop evidence that can then inform our policy recommendations. But also being a little bit creative, and thinking about how to use multiple methods together or to kind of piece together and develop new approaches based on existing methods that help us make sense of the data that we have, especially when that data is of poor quality.

Dr. Ian Anson

03:34

Yeah, that's fantastic. And certainly something that I've appreciated in my own work is many people come to the field of statistics and applied statistics and think that they're going to be able to take a data set and just immediately start running with it applying methodological tools. And they suddenly realize. no, the vast majority of the work that a lot of people are doing out there is just cleaning data and getting it ready for analysis. What is it about this data that's, that's so incomplete or so difficult to work with? What are some examples maybe of issues that you've encountered where you've had to apply some novel approaches to fixing the data.

Dr. Zoe McLaren

04:06

So a lot of the work that I've done on tuberculosis was using a data set of tuberculosis test results. And so when we think about kind of data quality issues, a lot of them fall into the category of missing data. And so that is missing data in terms of you have individuals that are in your data set. And maybe there isn't information recorded for all of the characteristics that you want to know about them. But also, you can think about missing data as the people who aren't in your data set, the data sets only capturing a subset of the entire population that you might be interested in. And so you want to kind of think about what to do about those two things. And with this tuberculosis data that I worked with, we had both of those issues, and and many more. And so what I was able to do, and part of it was saying, Okay, well maybe we don't have the universe of all the people that have TB, but we have the universe we know all the people who have been tested for TB and saying, well, maybe let's think about the policy recommendations for that group. What can we learn about them? And what policy recommendations can we bring into the game to address that? So in that case, we're actually being creative to abstract away from the missing data problem. And so even if we don't have that data, we're able to reframe the question a little bit, and then develop really good policy recommendations.

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