This Pittsburgh Team Developed a Shocking Model to Predict Measles Outbreaks—Are You Prepared?

In late 2014, California became an unwitting hotbed for a public health crisis when an outbreak of measles erupted at Disneyland, a theme park attracting millions of visitors from around the world. Many of the children visiting were not vaccinated against the disease, which is one of the most contagious known viruses. Within weeks, the outbreak extended beyond the park, leading to over 125 cases across California, as well as infections reported in six other states and even Mexico and Canada. This alarming situation marked the largest measles outbreak in California in two decades, prompting urgent action from state officials.

Then state Senator Richard Pan, a pediatrician by profession, spearheaded a controversial bill in early 2015 aimed at stripping away personal exemptions that allowed parents to opt out of vaccinating their children for non-medical reasons. As the illness spread and the winter tourist season approached, the urgency to safeguard public health became evident. Protests from opponents of the bill intensified, revealing a deep divide over vaccination mandates.

In an effort to bolster the case for his legislation, Dr. Pan enlisted the help of a research team from the University of Pittsburgh. This group utilized a pioneering method known as the FRED model—short for Framework for Reconstructing Epidemiological Dynamics—to simulate disease outbreaks based on various factors, including vaccination rates, population density, and commuting patterns. The detailed simulations illuminated how measles could spread in lawmakers’ districts, providing a stark visualization that ultimately helped persuade the legislature to pass the bill.

By mid-2015, California had made a significant policy shift, becoming the first state in nearly 30 years to eliminate personal and religious exemptions for vaccines, with the exception of medical reasons. As a result, the state’s vaccination rate for measles jumped more than three percentage points, reaching 96%, a figure that surpassed the herd immunity threshold essential to preventing disease spread. Dr. Pan noted, “We haven’t had a measles outbreak that large since.”

The Role of Predictive Modeling

The FRED model, which began development in 2001, is not merely a tool for policymaking; it represents a breakthrough in understanding how diseases can spread in complex environments. The concept was inspired by Fred Rogers, the beloved children’s television host, symbolizing the model's focus on communities and children. Initially funded through a $13.4 million grant from the National Institutes of Health, FRED has since been enhanced by private investments and has expanded its applications to include various public health forecasts beyond measles.

After the success in California, the Pitt team was called upon again in 2018 to predict measles outbreaks in Texas, which resulted in one of the deadliest outbreaks in the U.S. in over 30 years. The simulations revealed vulnerable areas throughout the state due to low vaccination rates. Short videos created by the team showcased how a single measles case could escalate rapidly, leading to widespread infection. However, despite these warnings, Texas lawmakers did not act on the findings, resulting in a tragic outbreak in 2025 that infected more than 800 people and led to the deaths of two children.

As Dr. Mark Roberts, a distinguished professor at the University of Pittsburgh’s School of Public Health and a key figure in the FRED team, stated, “If we presented the maps we created with a map of what happened in court, we'd be declared guilty of predicting this.” His work emphasizes the importance of preventive measures based on sound scientific data, especially in a climate where vaccination rates are declining.

While the FRED modeling has proven invaluable in predicting disease spread, it is not without limitations. The model relies on assumptions regarding human behavior, interaction, and accurate vaccination reporting. For instance, when applied to the COVID-19 pandemic, its effectiveness was compromised by unexpected societal changes like lockdowns. The model's predictions for the Texas measles outbreak accurately highlighted vulnerabilities but failed to pinpoint the rural epicenter where the outbreak ultimately surged.

The ongoing challenge of falling vaccination rates across the U.S. raises concerns about future outbreaks, particularly as parents increasingly opt out of vaccine mandates. As public health experts continue to observe these trends, the significance of models like FRED becomes even clearer. They not only help in anticipating potential crises but also in reinforcing the need for proactive measures to safeguard public health.

In conclusion, as we navigate public health challenges, the intersection of policy, community response, and predictive modeling will play a crucial role in determining our collective health outcomes. Dr. Roberts warns, “The risks from decreasing vaccination rates are clear. More children will be susceptible, there will be more cases, and more children will be at risk for potential serious health consequences—all of which are avoidable.” The lessons learned from California's experience with measles serve as a critical reminder of the importance of vaccination and the role of informed public policy in protecting community health.