This 17-year-old student built a math model that predicts how multiple diseases spread together inside the body

A 17-year-old student in Maryland has developed a mathematical model that could change the way public health officials study outbreaks involving more than one infectious disease.

Alyssa Yu, a senior at Poolesville High School in Montgomery County, created a computer-based framework that examines how two pathogens can spread through connected communities at the same time. Her work tackles a problem that has become increasingly important in a world where different disease outbreaks can overlap.

Also Read: He spent 16 years alone on one of world’s most remote islands: With two cats, chickens, a radio and basic supplies, this New Zealand sailor built a life in complete solitude


Yu's research was recognised at the 2026 Regeneron Science Talent Search, where she was named one of 40 national finalists from roughly 2,600 applicants across the US.

Her project, titled “When Epidemics Meet: Understanding Spatiotemporal Interactions of Two Pathogens on Metapopulation Networks via Reaction-Diffusion Dynamics,” combines epidemiology, mathematics and computer modelling to study what happens when two diseases circulate simultaneously.

Why two diseases can be harder to predict

Tracking a single infectious disease is already a complicated task. Public health researchers need to account for factors such as population movement, infection rates, geography and changes in transmission. The challenge becomes greater when another pathogen is spreading at the same time.

One disease may influence the behaviour of another, potentially changing how quickly infections rise or where outbreaks become concentrated. Flu and COVID-19, for example, can circulate during the same period, while people with one infection may also face the risk of another. Traditional models often focus on one pathogen independently. That can leave out important interactions between diseases.

Yu's research focuses on this gap.

How Alyssa Yu's model works

Instead of treating a large population as one group, Yu's framework breaks it into smaller connected areas. These could represent cities, towns or transportation hubs, with links between them reflecting how people move from one location to another.

This is known as a metapopulation network.

The model then follows the movement of two diseases through these interconnected populations. Yu uses reaction-diffusion mathematics, a mathematical approach commonly used to describe how substances spread and interact, to represent both the geographical movement of pathogens and their interactions within populations.

That combination allows the model to examine disease transmission across both space and time.

The calculations can also reveal locations where the interaction between two pathogens may produce unusually high infection levels. These potential hotspots could be important for health authorities trying to determine where an outbreak might accelerate.

Could it help target vaccines?

One of the most practical aspects of Yu's research is its potential use in vaccination planning. When vaccine supplies are limited during an outbreak, distributing doses equally across every location or demographic group may not always produce the greatest impact.

Yu tested different vaccination approaches through computer simulations. Her results suggested that targeting specific communities and population groups identified by the model could be more effective in some circumstances than distributing vaccines uniformly.

The framework can highlight areas and transport connections where the interaction between two pathogens is particularly strong. Health officials could potentially use that information to decide where limited vaccines or other resources should be deployed first.

The aim is not simply to predict where infections will appear. It is to help determine where intervention could make the biggest difference.

From high school research to national recognition

Yu's work earned her a place among the 40 finalists in the 2026 Regeneron Science Talent Search, one of the most prominent science and mathematics competitions for US high school students. The finalists were selected from about 2,600 students nationwide. According to competition records, the 2026 group represented 36 schools across 19 states.

The competition awards more than $1.8 million in prizes each year. Every finalist receives at least $25,000, while the top award is worth $250,000.

Yu also conducted part of her mathematical research through the MIT Program for Research in Mathematics, Engineering, and Science for High School Students, known as PRIMES-USA. She worked with academic mentor Laura P. Schaposnik.

Her interests extend beyond disease modelling. Yu is captain of the mathematics team at Poolesville High School and has been involved in organising regional maths competitions for younger students. She also leads environmental sustainability initiatives in her local community.

Why the research matters

Disease outbreaks rarely follow neat boundaries. People travel between cities, countries and continents every day, allowing pathogens to move between otherwise separate populations.

When different infections circulate at the same time, understanding those connections becomes even more important.

Yu's work offers one possible way to make disease forecasting more sophisticated by bringing together network-based epidemiology and reaction-diffusion mathematics.

The model is still a research framework rather than a replacement for real-world public health systems. But its approach points to a broader shift in epidemic modelling: instead of asking only how one disease spreads, researchers are increasingly looking at what happens when multiple pathogens meet.

For a high school student, Yu's project is an unusually advanced attempt to tackle that problem — and it could provide ideas for how future outbreak forecasting tools are built.