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

Epidemiology and Infection

Volumn 139, Issue 12, 2011, Pages 1854-1862

  Kaimi, I ^a   Diggle, P J ^a  

^a LANCASTER UNIVERSITY   (United Kingdom)

Author keywords

Gastrointestinal infections; mathematical modelling; prevention

Indexed keywords

ACCURACY; BAYES THEOREM; CONTROLLED STUDY; DISEASE SURVEILLANCE; FOOD POISONING; FORECASTING; GASTROINTESTINAL INFECTION; HUMAN; INCIDENCE; INFECTION RISK; POISSON DISTRIBUTION; PREDICTION; REVIEW; RISK REDUCTION; SEASONAL VARIATION; STOCHASTIC MODEL; TIME SERIES ANALYSIS; UNCERTAINTY; UNITED KINGDOM;

BAYES THEOREM; ENGLAND; FOODBORNE DISEASES; GASTROINTESTINAL DISEASES; HUMANS; INCIDENCE; MODELS, BIOLOGICAL; MONTE CARLO METHOD; POPULATION SURVEILLANCE; REGRESSION ANALYSIS; RISK ASSESSMENT; SPACE-TIME CLUSTERING; STOCHASTIC PROCESSES;

EID: 80255126148     PISSN: 09502688     EISSN: 14694409     Source Type: Journal    
DOI: 10.1017/S0950268811000057     Document Type: Review

References (14)

1. Diggle PJ, et al. On-line monitoring of public health surveillance data. In Monitoring the Health of populations: Statistical Principles and Methods for Public Health Surveillance, 2003, pp. 233-266. Oxford: Oxford University Press.
2. Diggle PJ, et al. Point process methodology for on-line spatio-temporal disease surveillance. Environmetrics 2005; 16: 423-434.
3. Paez M, Diggle PJ. Cox processes for estimating temporal variation in disease risk. Environmetrics 2009; 20: 981-1003.
4. Brix A, Diggle PJ. Spatiotemporal prediction for log- Gaussian Cox processes. Journal of the Royal Statistical Society, Series B 2001; 63: 823-841.
5. McCullagh P, Nelder J. Generalized Linear Models. London: Chapman and Hall, 1989
6. Chan K, Ledolter J. Monte Carlo EM estimation for time series models involving counts. Journal of the American Statistical Association 1995; 90: 242-252.
7. Hay J, Pettitt A. Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease. Biostatistics 2001; 2: 433-444.
8. Spiegelhalter D, et al. Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society Series B 2002; 64: 583-639.
9. Spiegelhalter D, et al. BUGS 0.5, Bayesian Inference using Gibbs Sampling. Manual (version ii). Cambridge: MRC Biostatistics Unit, 1996.
10. Cowles M, Carlin B. Markov chain Monte Carlo convergence diagnostics: a comparative study. Journal of American Statistical Association 1996; 91: 883-904.
11. Diggle PJ. Time Series: A Biostatistical Introduction. Oxford: Clarendon Press, 1990.
12. Zeger S. A regression model for time series of counts. Biometrika 1988; 75: 621-629.
13. Shephard N, Pitt MK. Likelihood analysis of non- Gaussian measurement time series. Biometrika 1997; 84: 653-667.
14. Box G, Jenkins G, Reinsel G. Time Series Analysis, Forecasting and Control, 3rd edn. San Francisco: Holden Day, 1994.