Abstract by Celeste Ingersoll
Bayesian 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 but several vaccines are in various stages of clinical trials. Currently, immunoprophylaxis is a preventative measure consisting of a series of monthly shots that should be administered at the start of and throughout peak RSV season. Thus, the successful implementation of immunoprophylaxis is contingent upon understanding when outbreak seasons will begin, peak, and end. In this research we estimate the seasonality of RSV using a spatially varying change point model. Further, using the fitted change point model, we develop an historical matching algorithm to generate real time predictions of seasonal curves for future years.