Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease.
This paper presents a Bayesian analysis of a time series of counts to assess its dependence on an explanatory variable. The time series represented is the incidence of the infectious disease ESBL-producing Klebsiella pneumoniae in an Australian hospital and the explanatory variable is the number of grams of antibiotic (third generation) cephalosporin used during that time. We demonstrate that there is a statistically significant relationship between disease occurrence and use of the antibiotic, lagged by three months. The model used is a parameter-driven model in the form of a generalized linear mixed model. Comparison of models is made in terms of mean square error.
Hay
J L
JL
Department of Social and Preventive Medicine, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, 4102, Australia. J.Hay@spmed.uq.edu.au
Pettitt
A N
AN