Dr Anthony Wood on data models of Covid-19 vaccine uptake
Developing hyper-specific data models, the boundaries between physics and epidemiology, and the freedom of being a scientist.
Dr Anthony Wood is a Post-Doctoral Research Fellow who specialises developing data models to understand viruses. In this interview, he talks about applying ideas from theoretical physics to understanding the spread of disease, using data to track Covid-19 infection and vaccine uptake in specific neighbourhoods, and the value of a friendly team.
Can you tell me about your work in a nutshell?
I've been at Roslin for around two years. In the first 18 months I was only looking at Covid-19, in particular, at the Covid-19 situation in Scotland, at really high resolution. Patterns in Covid are often reported quite broadly, but what I've been working on is looking at Covid-19 data at a really fine scale, in pockets of neighbourhoods with populations of around 500 to 1000 people.
So for example, instead of saying, ‘What's the Covid situation like in a particular city?’ We're saying ‘What's the Covid situation like in this very specific pocket of a city in comparison to their next-door neighbours?’, and really getting down into the details of the data. This helps to the risk factors behind testing positive, suffering more severe outcomes, and lower vaccine uptake. There was very substantial variation from neighbourhood to neighbourhood, and we try to understand why those differences were there.
What made you want to start studying Covid-19?
I'd say my route in epidemiology has been non-traditional. I did my PhD in theoretical physics here in Edinburgh, in the College of Science and Engineering. I then went into industry for 18 months during lockdown. During this time, I think I felt bit of an itch to get back into research after I finished my PhD and went straight into industry. It dawned on me that boundaries between subjects aren't as rigid as you may think, and so a lot of the work I do in epidemiology, certainly some of the projects I'm going to be doing soon can be tackled using techniques from theoretical physics. A lot of the methods used in theoretical physics can be used in epidemiology, and so I found that physics gave me a really good toolbox. When I saw an advert for a Covid-19 project, I thought I had never really considered epidemiology. I’m good with working with data, and I have a background working in finance roles I was pretty comfortable with high resolution data, and I also had that toolbox of being able to solve mathematical problems and understand complex systems, and Covid-19 is as complex a system as you could get.
What made you want to become a scientist in the first place?
I always thought I was going to be an engineer, because my family are predominantly engineers. And then I would find every time I would go into a lab or workshop it just wasn't for me, I'm not a hands-on kind of person. I think what makes me suited for going into science is just getting stuck into a problem - really enjoying the process of knowing nothing, and then at the end of the day, knowing something. What made me want to become a scientist was realising that it gives me the opportunity to look at interesting, complex problems.
What are some of the challenges you're facing in your job?
With coronavirus, one of the hardest things is that there is always more than one possible explanation for everything. For example, one thing we've looked at in particular has been with regards to vaccination uptake and trying to understand why some people choose to get vaccinated and why some people choose not to get vaccinated. If you take for example, young adults living in Edinburgh, vaccination uptake isn't extremely high. In the media, it’s often reduced to young people can't be bothered getting vaccinated, or they don't feel like it is important. However, things aren’t always as simple as they may seem. For example, many of those young adults are students who weren't living in Edinburgh during lockdown, they were living at home, and might have gotten vaccinated elsewhere. With every single outcome there are a multitude of different possible explanations, and one of the difficulties we have to avoid is falling into those pitfalls – instead, taking everything in and understanding that most things arise from a complex combination of several factors. So in a sentence, jumping to conclusions too early.
What would you say is your favourite part of your job?
One thing I love the most about the job I'm doing at the moment is the freedom to work on things that I find interesting, and our research group is extremely friendly. It sounds clichéd, but everybody is really supportive of one another. If you're stuck on something, somebody will take a couple of hours out of their day to help you with it.
Of course, there is some pressure to get work done, to put publications out, but it’s about freedom. You don’t get that in other professions.
What would you like to see in the future of your research?
One thing that the pandemic has really showed is the value of giving scientists access to data. Covid has been a unique instance where, due to the need for modelling support, we were given access to a lot of data that we wouldn't normally have access to, and we've gone and developed all these methods and insights.
There's absolutely nothing stopping us from doing the same to study other viruses, for example for influenza vaccine uptake or MMR uptake. That data is sitting somewhere, but researchers have never had access to it. Having greater access to the data would be the one thing I would want to see at the end of this, because there's a lot of really good stuff we've done and doesn't have to stop at Covid-19.
If you could give any advice to aspiring scientists, what would it be?
That’s funny, because I still consider myself to be an aspiring scientist. I guess this comes from doing my PhD. Often, when you're doing the work you don't see your progress, because there are a lot of hurdles along the way, and negative results where something didn't work. Research can be amazing even when it doesn’t work, because you can get to understand why it didn't work. A lot of the day-to-day research can be a bit demoralising, but once you look back at the end of the month or the end of the year, you’ll be able to see you did actually achieve a lot of work. It might not feel like a lot of the time, so I guess my advice would be to not get bogged down by the day-to-day of research. It’s all part of the path, sometimes research takes lots of twists and turns, but as long as you're going in the right direction in general, you're going to be okay.
If you were not a scientist, what would you like to be?
I would say the main thing I love to do outside of academia is running, and I'm really into cycling. Probably never professionally, but I would like to do something like that.