To the layperson, worldwide reports about the HIV/AIDS epidemic grow increasingly grim. But Benjamin Armbruster, an assistant professor at the McCormick School of Engineering and Applied Science, believes that good old-fashioned math can help solve problems of identification, treatment and prevention. A believer in the healthcare applications of engineering since grad school at Stanford, Armbruster focuses on HIV issues around the world, including Africa, India and right here at home. His most recent paper – accepted this week and available on his website – takes a look at HIV case-finding in sub-Saharan countries.
What is contact tracing?
Contact tracing finds you infected people: once you know somebody’s infected, you interview them about their sexual partners and try to follow up with them about getting tested.
Finding infected people is great because one of the big problems in Africa is that not enough people know their status. That’s part of the story of how it’s getting transmitted: people just don’t know they have it. You can only treat the people that you know are infected.
Right now screening programs rely on people to come to them. What are the benefits of this new idea?
It’s somewhat surprising that contact tracing might be a good idea because in these African countries, the prevalence of HIV isn’t that low. You’d think that having or expanding a testing program is a pretty reasonable thing to do. If, say, you have a testing site in Malawi (prevalence is around 10 percent) and random people come to your testing site – not really the case, but a decent idea – then one in 10 will test positive. Whereas if you do contact tracing, first of all you have to interview the people. Asking people who their sexual partners are is not easy. Second of all you have to find them, so it’s not obvious that this is a cheap way of going about things.
What makes this interesting is it turns out that the chance a sexual partner is infected is extremely high. So that kind of mitigates the increased costs – instead of a one in 10 chance, it’s like a one in two or so. So we’re quite excited that this might be something people want to try in the future.
How would this be better than current approaches?
With the screening program, at least to some extent, people get tested because they’re sick. Once they’re sick, it’s pretty late. In the early stages is where treatment helps the most. Treatment reduces their viral load, makes them less infectious, and averts secondary infections. They’re not spreading it.
You have a lot in the pipeline right now. What are some of the other ideas you’re looking at?
How frequently people should get tested for HIV. How HIV is spreading among gay men in India, and making predictions about how that’s changing over time, as the sexual roles become more fluid. How diseases are spreading on networks and how the network structure affects that spread. There are big problems – a lot of them – and I end up working on pieces of them.
What are the engineering aspects of all these projects?
There are a lot of prospective things where having a mathematical model either really helps, or is the only way you can do it – these are things where you wouldn’t want initially to do a clinical trial. Either the trial can’t answer your question or it would be expensive to do that.
The other way engineering comes in is the focus on costs: not only does this idea work, is it cheap? So for instance, with contact tracing, there’s a real focus on thinking, “What is the most cost-effective way of finding people that are infected?”
What still needs to happen?
Money helps. The other thing that would really help would be decreases in new infections, so some prevention programs that are really successful. What the money is doing is getting treatment to a lot of people, which is great. What it’s not been as successful at doing is decreasing the number of infections, and if you want to get stuff under control and decrease the prevalence of HIV, that’s really important.
