title: Detecting disease outbreaks using local spatiotemporal methods.
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.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
authors.forename: Yingqi
authors.initials: Y
authors.lastname: Zhao
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.forename: Amy H
authors.initials: AH
authors.lastname: Herring
authors.forename: Amy
authors.initials: A
authors.lastname: Ising
authors.forename: Anna
authors.initials: A
authors.lastname: Waller
authors.forename: David
authors.initials: D
authors.lastname: Richardson
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
mesh_terms: D000818:Animals; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D011159:Population Surveillance / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors