TY - JOUR

T1 - A hierarchical model for real-time monitoring of variation in risk of non-specific gastrointestinal infections

AU - Kaimi, I.

AU - Diggle, P. J.

N1 - http://journals.cambridge.org/action/displayJournal?jid=HYG The final, definitive version of this article has been published in the Journal, Epidemiology and Infection, 139 (12), pp 1854-1862 2011, © 2011 Cambridge University Press.

PY - 2011/12

Y1 - 2011/12

N2 - 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.

AB - 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.

KW - Gastrointestinal infections

KW - mathematical modelling

KW - prevention

KW - COX PROCESSES

KW - SERIES

KW - COUNTS

U2 - 10.1017/S0950268811000057

DO - 10.1017/S0950268811000057

M3 - Journal article

VL - 139

SP - 1854

EP - 1862

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 12

ER -