When people think about infectious disease outbreaks, it's easy to focus on the pathogen itself. But according to researchers at the University of Virginia's Biocomplexity Institute, understanding how diseases spread often has less to do with biology alone and more to do with the complex networks of people, places, and behaviors through which they move.
In the latest episode of Voices of Biocomplexity, host Erin Raymond sits down with computational epidemiologists Bryan Lewis and Srini Venkatramanan to explore two diseases that seem to have little in common: measles and Ebola.
At first glance, the comparison appears unusual. Measles is one of the most contagious diseases known and is resurging in communities with declining vaccination rates, including recent outbreaks much closer to home. Ebola, by contrast, spreads far less easily but carries a much higher fatality rate and continues to challenge public health officials in regions where conflict, limited healthcare infrastructure, and disrupted surveillance make containment extraordinarily difficult.
Despite these differences, both diseases illustrate the same central lesson: outbreaks are shaped not only by viruses but also by the systems in which people live.
"These are really two different public health landscapes," the researchers explain during the conversation. Measles highlights the importance of maintaining high vaccination coverage across entire populations, while Ebola often requires highly targeted interventions such as contact tracing, isolation, and ring vaccination around known cases.
The discussion also offers a behind-the-scenes look at how computational models support public health decision making. Rather than attempting to predict a single future, researchers build models that allow them to explore "what if" scenarios. What happens if vaccination rates increase? What if contact tracing begins sooner? What if travel patterns change or healthcare access is disrupted?
These models integrate information from many different sources, allowing scientists to evaluate potential interventions before decisions are made in the real world.
The conversation also explores how modeling approaches change as an outbreak evolves. Early in an Ebola outbreak, researchers may use detailed agent-based models that simulate interactions between individuals. As the disease spreads across larger regions, broader population-level models become more effective for understanding regional transmission and evaluating response strategies.
Throughout the episode, Lewis and Venkatramanan emphasize that infectious disease modeling is ultimately about helping decision makers navigate uncertainty. Every outbreak presents difficult choices, but computational models provide a way to test possible responses safely, revealing how small changes in policy or behavior can dramatically alter outcomes.
It's a reminder that preparing for the next outbreak requires more than understanding pathogens. It requires understanding people, networks, infrastructure, trust, and the interconnected systems that shape how diseases spread.
Listen to the full episode of Voices of Biocomplexity to hear how computational epidemiology is helping researchers answer one of public health's most important questions: not simply what might happen next, but what we can do to change the outcome.