title: Improved hidden Markov model for nosocomial infections.
abstract: We propose a novel hidden Markov model (HMM) for parameter estimation in hospital transmission models, and show that commonly made simplifying assumptions can lead to severe model misspecification and poor parameter estimates. A standard HMM that embodies two commonly made simplifying assumptions, namely a fixed patient count and binomially distributed detections is compared with a new alternative HMM that does not require these simplifying assumptions. Using simulated data, we demonstrate how each of the simplifying assumptions used by the standard model leads to model misspecification, whereas the alternative model results in accurate parameter estimates.
authors.affiliation: Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT, USA karim.khader@hsc.utah.edu.
authors.forename: Karim
authors.initials: K
authors.lastname: Khader
authors.affiliation: Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT, USA.
authors.forename: Molly
authors.initials: M
authors.lastname: Leecaster
authors.affiliation: Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT, USA.
authors.forename: Tom
authors.initials: T
authors.lastname: Greene
authors.affiliation: Division of Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: Samore
authors.affiliation: Division of Genetic Epidemiology, School of Medicine, University of Utah, Salt Lake City, UT, USA.
authors.forename: Alun
authors.initials: A
authors.lastname: Thomas
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003428:Cross Infection / Q000453:epidemiology* / Q000635:transmission*; D006801:Humans; D008390:Markov Chains; D055641:Mathematical Concepts; D008954:Models, Biological; D015995:Prevalence
keywords: detection process; observation model