A0971
Title: Spatiotemporal surveillance of infectious diseases by statistical process control charts
Authors: Peihua Qiu - University of Florida (United States) [presenting]
Abstract: Online sequential monitoring of the incidence rates of infectious diseases is critically important for public health. Governments have spent many resources building global, national, and regional disease reporting and surveillance systems. In these systems, conventional control charts, such as the cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) charts, are routinely included for disease surveillance purposes. However, these charts require many assumptions on the observed data that are rarely valid in practice, making their results unreliable to use. The purpose is to present a recent sequential monitoring approach for spatiotemporal disease surveillance, which can accommodate the dynamic nature of the observed disease incidence rates, spatiotemporal data correlation, and nonparametric data distribution. It is shown that the new method is more reliable than the commonly used conventional control charts for spatiotemporal surveillance of infectious diseases.
