A 17-year-old student from Maryland has developed a new mathematical framework to predict how multiple infectious diseases interact and spread through connected populations at the same time. Alyssa Yu, a senior at Poolesville High School in Montgomery County, created the computer model to address a major gap in public health forecasting. Most existing disease models track one pathogen at a time and do not fully account for what happens when several outbreaks occur together. Her research earned her a place among the 40 national finalists in the 2026 Regeneron Science Talent Search, the oldest and one of the most prestigious science and maths competitions for US high school seniors. The project, titled “When Epidemics Meet: Understanding Spatiotemporal Interactions of Two Pathogens on Metapopulation Networks via Reaction-Diffusion Dynamics,” could help public health officials plan more targeted vaccination campaigns during outbreaks involving multiple diseases.
Understanding how diseases interact
Public health agencies often use computer models to predict how quickly diseases spread and where new infection clusters could appear. But when two infectious diseases spread through the same population, one disease can change how the other behaves. Examples include seasonal flu spreading alongside COVID-19 or secondary infections occurring alongside HIV. The presence of one pathogen can affect the other disease’s spread, severity and overall impact. Standard models designed for a single disease often struggle to capture these combined effects. Epidemiologists refer to such situations as co-epidemics or “twindemics”.Yu addressed the problem by creating a reaction-diffusion model that works across metapopulation networks. These models divide large geographic areas into smaller connected sections, such as towns, cities or transport hubs, that are linked by people’s movement. To model how two diseases move through these connected areas, Yu used mathematical methods that are also used to study chemical reactions. Her framework tracks how diseases spread across space and time while also showing how they interact inside human hosts. By studying the model’s complex mathematical equations, she identified possible geographic “hot spots” where interactions between the two diseases could cause infection rates to rise much higher than expected.
Planning targeted vaccination
Yu’s model does more than identify areas where outbreaks could grow. It also shows how health officials could use limited medical resources more efficiently when two diseases are spreading at the same time. She ran simulations involving different vaccination strategies. The results showed that giving vaccines evenly across an entire population can be less effective than targeting specific groups and locations identified by the model. By identifying demographic groups and transport hubs where interactions between the two pathogens are strongest, the model could help officials design more focused vaccination campaigns. These strategies could slow infection more quickly while requiring fewer vaccine doses.
Recognition in national competition
Yu was selected as one of 40 finalists from about 2,600 high school applicants across the United States. Her research was evaluated by a panel of scientists organised by the Society for Science. According to competition records, the 2026 finalists came from 36 schools across 19 states. The competition awards more than 1.8 million dollars in prizes each year. Every finalist receives at least 25,000 dollars, while the top prizes can reach 250,000 dollars. Yu carried out part of her advanced mathematical research through the Massachusetts Institute of Technology Program for Research in Mathematics, Engineering, and Science for High School Students (PRIMES-USA), working with academic mentor Laura P. Schaposnik. Alongside her research in computational biology and bioinformatics, official profiles from Montgomery County Public Schools and the Society for Science note that Yu is captain of the Poolesville High School maths team. She also organises regional maths competitions for middle school students and leads local environmental sustainability programmes.
A new tool for disease forecasting
Mathematical modelling has become an important part of preparing for epidemics and other public health emergencies. As international travel, global trade and growing cities make overlapping outbreaks more common, models that can track several diseases at once could give health agencies a major advantage. Yu’s framework provides a possible foundation for improving real-time disease forecasting tools. By combining ideas from chemical reaction modelling with network-based epidemiology, her research shows how advanced mathematics can turn disease data into practical strategies for controlling outbreaks.