A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections

I. Kaimi, P. J. Diggle

Research output: Contribution to journalArticle

Abstract

The AEGISS (Ascertainment and Enhancement of Disease Surveillance and Statistics) project uses spatio-temporal statistical methods to identify anomalies in the incidence of gastrointestinal infections in the UK. The focus of this paper is the modelling of temporal variation in incidence using data from the Southampton area in southern England. We identified and fitted a hierarchical stochastic model for the time series of daily incident cases to enable probabilistic prediction of temporal variation in risk, and demonstrated the resulting gains in predictive accuracy by comparison with a conventional analysis based on an over-dispersed Poisson log-linear regression model. We used Bayesian methods of inference in order to incorporate parameter uncertainty in our predictive inference of risk. Incorporation of our model in the overall spatio-temporal model, will contribute to the accurate and timely prediction of unusually high food-poisoning incidence, and thus to the identification and prevention of future outbreaks.

Original languageEnglish (US)
Pages (from-to)1854-1862
Number of pages9
JournalEpidemiology and Infection
Volume139
Issue number12
DOIs
StatePublished - Dec 2011
Externally publishedYes

Fingerprint

Infection
Linear Models
Incidence
Foodborne Diseases
Bayes Theorem
England
Uncertainty
Disease Outbreaks

Keywords

  • Gastrointestinal infections
  • mathematical modelling
  • prevention

ASJC Scopus subject areas

  • Infectious Diseases
  • Epidemiology

Cite this

A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections. / Kaimi, I.; Diggle, P. J.

In: Epidemiology and Infection, Vol. 139, No. 12, 12.2011, p. 1854-1862.

Research output: Contribution to journalArticle

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