A Bayesian approach to real-time spatiotemporal prediction systems for bronchiolitis. (August 2021)

• Record Type: Journal Article
• Authors: Heaton, Matthew J.; Ingersoll, Celeste; Berrett, Candace; Hartman, Brian M.; Sloan, Chantel
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 38(2021)
• Publication Date: 2021-08
• Subjects: Spatiotemporal predictions -- Markov chain Monte Carlo -- Change point model; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2021.100434
• Languages: English
• ISSNs: 1877-5845

Abstract

Abstract: Respiratory Syncytial Virus (RSV) induced bronchiolitis is a common lung infection and a major 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, 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 seasonal epidemic curves of RSV induced bronchiolitis using a spatially varying change point model. Further, in a novel approach and using the fitted change point model, we develop a historical matching algorithm to generate real time predictions of seasonal curves for future years.