TCNJ mathematics professor Jana Gevertz is participating in a federally funded study to improve understanding of chronic Lyme disease, especially its effects on the brain and nervous system. The project brings together researchers from The College of New Jersey (TCNJ), UCLA, and the nonprofit MyLymeData.org, with funding from the U.S. Department of Defense.
The research targets patients who continue to experience symptoms after initial treatment or those who were not diagnosed or treated during the early stages of Lyme disease. The goal is to identify and define observable traits or symptom patterns—known as phenotypic characteristics—in people who develop neurological forms of the illness.
“Before you can effectively study or treat a subgroup of patients, you need to define who they are,” Gevertz says. “Right now, ‘chronic Lyme’ is an umbrella term. One of our key goals is to better define what persistent neurological Lyme actually looks like across the patient population.”
Researchers are looking at symptoms such as brain fog, memory loss, headaches, difficulty concentrating, sleep disturbances, sensory sensitivity, and motor control issues. These symptoms are often reported by patients but remain poorly understood and inconsistently recognized in medical literature.
“These are symptoms patients live with every day, but without a formal framework, doctors may dismiss them or treat them generically,” Gevertz explains. “By identifying consistent symptom patterns, we can begin to group patients in meaningful ways, and that’s the first step toward better diagnosis and more targeted treatments.”
Gevertz is using machine learning tools and statistical methods to analyze extensive patient-reported data collected by MyLymeData.org. Unlike previous studies that required strict criteria such as a specific rash or positive CDC-standard blood test for inclusion—which could exclude many real cases—this project uses self-reported data from a broader range of clinically diagnosed individuals.
“Those older criteria exclude up to 90% of actual patients,” Gevertz says. “This study uses self-reported data, which isn’t perfect, but it’s the most comprehensive picture we have of people actually living with the disease.”
Drawing on her own experience with chronic Lyme disease for over ten years, Gevertz describes her work as bridging mathematical analysis with medical questions. She also mentors undergraduate students at UCLA working on computational aspects this summer and plans similar opportunities for TCNJ students next year.
The Department of Defense has shown interest in this research due to increasing rates of tick-borne illnesses among military personnel.
“It’s become a readiness issue,” Gevertz explains. “But the implications of this work extend far beyond the military.”
In future phases, researchers hope their findings will help guide more personalized treatments for chronic Lyme sufferers by recognizing specific patterns within patient groups—a method already used in cancer care based on tumor characteristics. For now, Gevertz views this research as progress toward recognition for those affected by chronic Lyme disease.
“These patients have been dismissed and told there is ‘nothing wrong with them.’ But they’re sick, and they deserve answers,” she says. “This research is about helping make that happen.”
Funding comes through the Assistant Secretary of Defense for Health Affairs via the Tick-Borne Disease Research Program under Award No. HT9425-25-1-0351; conclusions represent those of the authors rather than official Department policy.
