title: A spatio-temporal absorbing state model for disease and syndromic surveillance.
abstract: Reliable surveillance models are an important tool in public health because they aid in mitigating disease outbreaks, identify where and when disease outbreaks occur, and predict future occurrences. Although many statistical models have been devised for surveillance purposes, none are able to simultaneously achieve the important practical goals of good sensitivity and specificity, proper use of covariate information, inclusion of spatio-temporal dynamics, and transparent support to decision-makers. In an effort to achieve these goals, this paper proposes a spatio-temporal conditional autoregressive hidden Markov model with an absorbing state. The model performs well in both a large simulation study and in an application to influenza/pneumonia fatality data.
authors.affiliation: Department of Statistical Science, Duke University, Box 90251, Durham, NC, 27708-0251, USA. matt@stat.duke.edu
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Heaton
authors.forename: David L
authors.initials: DL
authors.lastname: Banks
authors.forename: Jian
authors.initials: J
authors.lastname: Zou
authors.forename: Alan F
authors.initials: AF
authors.lastname: Karr
authors.forename: Gauri
authors.initials: G
authors.lastname: Datta
authors.forename: James
authors.initials: J
authors.lastname: Lynch
authors.forename: Francisco
authors.initials: F
authors.lastname: Vera
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D008390:Markov Chains; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D013029:Space-Time Clustering; D013577:Syndrome; D014481:United States / Q000453:epidemiology