Title: Detecting disease outbreaks using local spatiotemporal methods.

Authors: Zhao, Yingqi; Zeng, Donglin; Herring, Amy H; Ising, Amy; Waller, Anna; Richardson, David; Kosorok, Michael R

Published In Biometrics, (2011 Dec)

Abstract: A real-time surveillance method is developed with emphasis on rapid and accurate detection of emerging outbreaks. We develop a model with relatively weak assumptions regarding the latent processes generating the observed data, ensuring a robust prediction of the spatiotemporal incidence surface. Estimation occurs via a local linear fitting combined with day-of-week effects, where spatial smoothing is handled by a novel distance metric that adjusts for population density. Detection of emerging outbreaks is carried out via residual analysis. Both daily residuals and AR model-based detrended residuals are used for detecting abnormalities in the data given that either a large daily residual or an increasing temporal trend in the residuals signals a potential outbreak, with the threshold for statistical significance determined using a resampling approach.

PubMed ID: 21418049

MeSH Terms: No MeSH terms associated with this publication