Abstract by Celeste Ingersoll

Personal Infomation

Presenter's Name

Celeste Ingersoll

Degree Level


Abstract Infomation



Faculty Advisor

Matthew Heaton


Baysian Approach to Real-time Spatiotemporal Prediction Systems for Respiratory Syncytial Virus


Respiratory Syncytial Virus (RSV) is an increasingly common cause of infant hospitalization and mortality. Unfortunately, there is no known cure for RSV. Fortunately, there is a known medicine to help prevent RSV in infants. This preventative measure is a series of monthly shots that should be administered at the start and throughout RSV season. Thus, it is important for each state in the United States to understand when outbreak season will begin, peak, and end. It is our goal to estimate the seasonality and understand the behavior of this virus across space and time. With this understanding, we can make predictions about the upcoming flu season in order to help health care providers prepare for the next RSV outbreak and know when to begin administering these shots to at-risk children.  
Bayesian and spatiotemporal methods are utilized to accurately calculate estimates and make predictions based on data from counties arcross the United States, aggregated to a state level.