A0746
Title: Sequential Bayesian spatiotemporal outbreak detection
Authors: Frank Zou - University of Central Florida (United States) [presenting]
Abstract: Early online outbreak detection for an epidemic is vital for disease-control authorities to make policies for the protection of public health and normal socioeconomic functions. Modern public health streaming surveillance data are often collected from multiple data sources, exhibiting spatio-temporal interdependence and imbalance issues. To address those issues, a Bayesian online spatiotemporal outbreak detection is proposed with prior updating and p-value adaptation (BOSTON-PUPA) procedure. Using sequential p-value combinations, this iterative procedure involves the generalized Poisson distribution (GPD) model and supports synchronous surveillance over multiple locations, with a controlled false detection rate as well as high sensitivity against outbreaks in a wide range of signal-to-noise ratios. In the simulation study, several popular combined p-value methods in the BOSTON-PUPA procedure are employed and compared based on sensitivity, specificity, false detection rate, and delay before making recommendations. The method is illustrated by detecting the outbreaks in the real COVID-19 daily case count data in Massachusetts counties in 2020.
