Xin Hui Chan: Using big data to eliminate malaria

My name is Xin Hui Chan, I’m a doctor working in Bangkok at the Mahidol Oxford Tropical Medicine Research Unit. I work on the safety of antimalarial medicines, in particular the application to the elimination of malaria.

Malaria is the most important parasitic disease of humans and antimalarial medicines have a very important role in the control and elimination of malaria. They do so primarily through three ways: one is through saving lives by reducing mortality, the second is by reducing the duration for which people are ill, and lastly and also very importantly by preventing the continued spread of malaria to other people, and this includes resistant strains for which there is a lot of concern at the moment.

What I do is look at the safety of these medicines, and as you can imagine, because they are used on a really massive scale - we’re talking about hundreds of millions of courses, this is entire treatment courses of antimalarial medicines used every year - there’s quite a bit of data we deal with and it’s a bit like a jungle of data. One of the big things I do in my work is to try to find a way to make sense of it, to give it a framework, to put it together and to analyse it, to help us understand whether any concerns about the safety are problematic or not. What we then do it to provide this information to policy makers and stakeholders to assist them with making decisions about how best to use antimalarial medicines in their own malaria elimination programmes.

Real world data is usually not in the format that you want it to be. One of the main challenges in my work is to find a unified structure which allows me to standardise this data to conduct a scientifically rigorous analysis. Often a lot of thinking goes into ‘how do I set up something which is scientifically rigorous and reproducible’, this is an important part of the work. It’s actually really challenging and also it can be very fun.

One of the things which has happened is this massive explosion of clinical data. People are doing studies on bigger scales than ever before producing lots of data, and alongside this we see the development of more and more powerful computers and statistical techniques. We try very hard to unify these three lines of development, and to try to make these developments useful to answer important clinical questions for malaria elimination. It’s something I’m very passionate about, it’s something I’m feeling very privileged to be a part of, and I have to say despite some of the challenging bits day to day, it’s something which I think is very meaningful.

Malaria is the most important parasitic infection to still affect humans and thousands of people die from it every day, even though we have perfectly good and effective treatments, which are safe to treat it. Despite all of this, one of the concerns which has emerged recently is that progress against malaria has slowed, and we do need information and evidence to support the best application of currently available tools to enable us to eliminate malaria.

My work is useful in that it aspires and aims very much to be a bridge between science and policy, by addressing questions raised by policy makers which could be stumbling blocks or barriers to the use of antimalarials in malaria elimination. This sort of work, in my opinion, is cost effective, it’s very responsive to policy requirements and it’s very translational in that we aim specifically to bridge the gap between science and policy. In the time I’ve been here our work has already been able to impact both the practice and policy of the use of antimalarials, in particular in relation to their safety.