Detecting disease outbreaks using local spatiotemporal methods.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Zhao, Y; Zeng, D; Herring, AH; Ising, A; Waller, A; Richardson, D; Kosorok, MR

Published Date

  • December 2011

Published In

Volume / Issue

  • 67 / 4

Start / End Page

  • 1508 - 1517

PubMed ID

  • 21418049

Pubmed Central ID

  • PMC3698245

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.1111/j.1541-0420.2011.01585.x


  • eng