Q&A With Uptake’s Sasha Gutfraind: Hepatitis C Insights From Data-Driven Simulation Models

Our Chief Healthcare Data Scientist, Sasha Gutfraind, is collaborating closely with epidemiologists at the University of Illinois at Chicago and Loyola University Medical Center on a project commissioned by the FDA​ that takes a look at what’s happening with Hepatitis C in the Chicagoland area.

Uptake data scientists spend time working on relevant data science projects outside of Uptake. Whether they’re using their skillsets to help non-profits or pursuing research to get their Ph.D., they’re continuously stretching their capabilities when it comes to solving pressing business and social problems.

Our Chief Healthcare Data Scientist, Sasha Gutfraind, is collaborating closely with epidemiologists at the University of Illinois at Chicago and Loyola University Medical Center on a project commissioned by the FDA that takes a look at what’s happening with Hepatitis C in the Chicagoland area. At the end of the project, they hope to help epidemiologists better understand the course of the disease.

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We sat down with Sasha to learn more about what he’s doing.

What prompted you to get involved in data-driven simulation models for Hepatitis C research?

I worked on using simulations for security problems in the past and enjoyed the work. When the FDA commissioned this project, which was to simulate infections in a population to get an understanding of how the disease will progress, I thought it would be a perfect fit for my experience. Beyond tapping my technical expertise and field interests, it could greatly impact the community.

Could you explain how the simulation models work?

Simulations are an incredibly powerful tool. Like a massive multi-person game, they allow a computer program to create a microcosm of the real world. In epidemiology, the simulation represents individual people, their health condition and how a disease might spread from person-to-person through interactions.

Running a simulation performs a calculation, which predicts the future of a disease outbreak and shows what would happen if a new treatment is introduced. The core to building accurate simulations is to gather and use a lot of detailed data about the disease. This was step one in our process.

What work was necessary on the data-gathering side to get started on a project of this scope?

We constructed and combined years of health, geographic and other important datasets from the Chicagoland area. Beyond gathering existing data, we interviewed drug users to get a clear understanding of how they interact with one another. To date, this simulation is one of the most data-rich Hepatitis C simulations ever constructed.

What insights have you gathered thus far?

So far, we’ve determined who might be infected with Hepatitis C in the future and found a few surprises. Although Hepatitis C is highest among minority groups living in the inner cities, the simulation suggests that if the disease maintains its course without any intervention, suburban, adolescent males would be at higher risk because research shows they are experimenting with injection drugs. Additionally, the overall pervasiveness of Hepatitis C in Chicago is expected to decline throughout the next 20 years.

How is this project helping the Chicagoland area?

Such long-term epidemiological forecasts can be used by the city and public health authorities to better understand the future and make Chicago healthier. For example, city and public health authorities can run targeted campaigns to these populations to help prevent infections and create more awareness around the implications of the disease.

Sasha and researchers plan to use this simulation tool to investigate specific ways to slow down the spread of Hepatitis C and forecast the effects of antiviral drugs. At the end of the project, they hope to find the most cost-effective method of combining the drugs with other interventions.

Interested in learning more about what projects Uptakers are working on? Check out this recent Q&A with Uptake Data Scientist Yuan Tang about his work on Scikit Flow.