Cargando…

Modeling and detection of respiratory-related outbreak signatures

BACKGROUND: Time series methods are commonly used to detect disease outbreak signatures (e.g., signals due to influenza outbreaks and anthrax attacks) from varying respiratory-related diagnostic or syndromic data sources. Typically this involves two components: (i) Using time series methods to model...

Descripción completa

Detalles Bibliográficos
Autores principales: Craigmile, Peter F, Kim, Namhee, Fernandez, Soledad A, Bonsu, Bema K
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2203979/
https://www.ncbi.nlm.nih.gov/pubmed/17919318
http://dx.doi.org/10.1186/1472-6947-7-28
_version_ 1782148405835857920
author Craigmile, Peter F
Kim, Namhee
Fernandez, Soledad A
Bonsu, Bema K
author_facet Craigmile, Peter F
Kim, Namhee
Fernandez, Soledad A
Bonsu, Bema K
author_sort Craigmile, Peter F
collection PubMed
description BACKGROUND: Time series methods are commonly used to detect disease outbreak signatures (e.g., signals due to influenza outbreaks and anthrax attacks) from varying respiratory-related diagnostic or syndromic data sources. Typically this involves two components: (i) Using time series methods to model the baseline background distribution (the time series process that is assumed to contain no outbreak signatures), (ii) Detecting outbreak signatures using filter-based time series methods. METHODS: We consider time series models for chest radiograph data obtained from Midwest children's emergency departments. These models incorporate available covariate information such as patient visit counts and smoothed ambient temperature series, as well as time series dependencies on daily and weekly seasonal scales. Respiratory-related outbreak signature detection is based on filtering the one-step-ahead prediction errors obtained from the time series models for the respiratory-complaint background. RESULTS: Using simulation experiments based on a stochastic model for an anthrax attack, we illustrate the effect of the choice of filter and the statistical models upon radiograph-attributed outbreak signature detection. CONCLUSION: We demonstrate the importance of using seasonal autoregressive integrated average time series models (SARIMA) with covariates in the modeling of respiratory-related time series data. We find some homogeneity in the time series models for the respiratory-complaint backgrounds across the Midwest emergency departments studied. Our simulations show that the balance between specificity, sensitivity, and timeliness to detect an outbreak signature differs by the emergency department and the choice of filter. The linear and exponential filters provide a good balance.
format Text
id pubmed-2203979
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-22039792008-01-17 Modeling and detection of respiratory-related outbreak signatures Craigmile, Peter F Kim, Namhee Fernandez, Soledad A Bonsu, Bema K BMC Med Inform Decis Mak Research Article BACKGROUND: Time series methods are commonly used to detect disease outbreak signatures (e.g., signals due to influenza outbreaks and anthrax attacks) from varying respiratory-related diagnostic or syndromic data sources. Typically this involves two components: (i) Using time series methods to model the baseline background distribution (the time series process that is assumed to contain no outbreak signatures), (ii) Detecting outbreak signatures using filter-based time series methods. METHODS: We consider time series models for chest radiograph data obtained from Midwest children's emergency departments. These models incorporate available covariate information such as patient visit counts and smoothed ambient temperature series, as well as time series dependencies on daily and weekly seasonal scales. Respiratory-related outbreak signature detection is based on filtering the one-step-ahead prediction errors obtained from the time series models for the respiratory-complaint background. RESULTS: Using simulation experiments based on a stochastic model for an anthrax attack, we illustrate the effect of the choice of filter and the statistical models upon radiograph-attributed outbreak signature detection. CONCLUSION: We demonstrate the importance of using seasonal autoregressive integrated average time series models (SARIMA) with covariates in the modeling of respiratory-related time series data. We find some homogeneity in the time series models for the respiratory-complaint backgrounds across the Midwest emergency departments studied. Our simulations show that the balance between specificity, sensitivity, and timeliness to detect an outbreak signature differs by the emergency department and the choice of filter. The linear and exponential filters provide a good balance. BioMed Central 2007-10-05 /pmc/articles/PMC2203979/ /pubmed/17919318 http://dx.doi.org/10.1186/1472-6947-7-28 Text en Copyright © 2007 Craigmile et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Craigmile, Peter F
Kim, Namhee
Fernandez, Soledad A
Bonsu, Bema K
Modeling and detection of respiratory-related outbreak signatures
title Modeling and detection of respiratory-related outbreak signatures
title_full Modeling and detection of respiratory-related outbreak signatures
title_fullStr Modeling and detection of respiratory-related outbreak signatures
title_full_unstemmed Modeling and detection of respiratory-related outbreak signatures
title_short Modeling and detection of respiratory-related outbreak signatures
title_sort modeling and detection of respiratory-related outbreak signatures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2203979/
https://www.ncbi.nlm.nih.gov/pubmed/17919318
http://dx.doi.org/10.1186/1472-6947-7-28
work_keys_str_mv AT craigmilepeterf modelinganddetectionofrespiratoryrelatedoutbreaksignatures
AT kimnamhee modelinganddetectionofrespiratoryrelatedoutbreaksignatures
AT fernandezsoledada modelinganddetectionofrespiratoryrelatedoutbreaksignatures
AT bonsubemak modelinganddetectionofrespiratoryrelatedoutbreaksignatures