Gabrielle Ferra

Gabrielle Ferra, a senior applied mathematics-biology major at Brown University, integrated epidemiological and evolutionary models into a teaching tool to prepare for future pandemics.

Learning about infectious diseases across all academic levels is vital for outbreak preparation and STEM education. Before this summer, I had never been in such an interdisciplinary and collaborative environment with a diverse group of scientists working towards one goal: to learn about and treat human disease. I have always had an interest in medical research, and have witnessed the importance of medical research first hand, so it was an honor to be part of an institute whose sole mission is to advance this field.For that reason, we created a smartphone app that realistically simulates outbreaks by spreading a pathogen through Bluetooth technology that mimics airborne infections. In order to simulate realistic pathogens, we need to determine the rates of infection and recovery, along with other disease-defining parameters of the epidemic models driving the simulations. This presents unique challenges as infectious disease outbreaks exhibit complex, nonlinear, and stochastic dynamics. In addition, accounting for pathogen evolution is critical when modeling an outbreak, as pathogens are known for quickly evolving into multiple strains during an outbreak, affecting how populations are diagnosed and treated. Thus, the goal of this study is to construct an accurate parameter-fitting approach that will generate stochastic epidemic models, simulating realistic outbreak data and taking evolutionary change into account. Our research uses a partially observed Markov process (POMP) model, which captures the stochasticity and reality of underreported data during outbreaks, to successfully estimate parameters from past outbreak data. We have successfully used this model to estimate the parameters from mumps outbreaks on college campuses. Subsequently, we use these estimated parameters to simulate outbreak data. To account for evolutionary dynamics, our next steps are to incorporate intrahost viral variation and mutation rates in the model. Ultimately, our work will result in an effective teaching tool that allows students to learn about preventing and controlling outbreaks without having to wait for an actual pandemic. 

Project: Simulating disease outbreaks to prepare for future pandemics

Mentor: Andres Colubri, Sabeti Lab