title: A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections.
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.
authors.affiliation: Department of Medicine, School of Health and Medicine, Lancaster University, UK. i.kaimi@lancaster.ac.uk
authors.forename: I
authors.initials: I
authors.lastname: Kaimi
authors.forename: P J
authors.initials: PJ
authors.lastname: Diggle
mesh_terms: D001499:Bayes Theorem; D004739:England / Q000453:epidemiology; D005517:Foodborne Diseases / Q000453:epidemiology* / Q000382:microbiology / Q000517:prevention & control; D005767:Gastrointestinal Diseases / Q000453:epidemiology* / Q000382:microbiology / Q000517:prevention & control; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D009010:Monte Carlo Method; D011159:Population Surveillance; D012044:Regression Analysis; D018570:Risk Assessment; D013029:Space-Time Clustering; D013269:Stochastic Processes