Predicting Outbreaks: Supercomputers Help Forecast Diseases’ Spread

How an infectious disease spreads depends on the social lives of the people living near the outbreak. Using math, advanced statistics, and high-powered supercomputers, it’s possible to model the complex web of daily interactions that occur at school, home, work, healthcare settings, and in the community to predict the way diseases like influenza spread.


Knowing where, when, and how quickly a dangerous pathogen is likely to spread is key to controlling an outbreak. The Texas Pandemic Flu Toolkit is an online resource that allows public health officials to simulate pandemics on supercomputers at UT’s Texas Advanced Computing Center (TACC). The toolkit also includes decision-support tools that help officials plan for future crises, determine how and where to use limited supplies of medicines and vaccines, and deploy other disease-fighting resources.

When a relatively new and deadly disease like Ebola unfolds on the other side of the globe, forecasting can be difficult. Dr. Lauren Meyers and colleagues have projected the current Ebola epidemic using a combination of epidemiological data (numbers of cases occurring each day) and genomic sequences from the circulating Ebola virus. Studies of prior Ebola outbreaks in Africa suggest that a large number of people may have been immunized by exposure to Ebola without getting sick. Dr. Meyers’ research team is trying to determine whether silent immunity exists, in the hopes of improving forecasts and the ability of doctors and nurses on the frontlines to more safely treat sick patients.

Meet the Scientist

Lauren Ancel Meyers
Mathematical Biologist