title: Spatio-temporal modelling of disease incidence with missing covariate values.
abstract: The search for an association between disease incidence and possible risk factors using surveillance data needs to account for possible spatial and temporal correlations in underlying risk. This can be especially difficult if there are missing values for some important covariates. We present a case study to show how this problem can be overcome in a Bayesian analysis framework by adding to the usual spatio-temporal model a component for modelling the missing data.
authors.affiliation: The Institute of Fundamental Sciences, Massey University,Palmerston North,New Zealand.
authors.forename: R C
authors.initials: RC
authors.lastname: Holland
authors.affiliation: The Institute of Fundamental Sciences, Massey University,Palmerston North,New Zealand.
authors.forename: G
authors.initials: G
authors.lastname: Jones
authors.affiliation: Molecular Epidemiology and Public Health Laboratory, Institute of Veterinary, Animal and Biomedical Sciences, Massey University,Palmerston North,New Zealand.
authors.forename: J
authors.initials: J
authors.lastname: Benschop
mesh_terms: D001499:Bayes Theorem; D002169:Campylobacter Infections / Q000453:epidemiology*; D003457:Cryptosporidiosis / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D009520:New Zealand / Q000453:epidemiology; D011891:Rain; D012307:Risk Factors; D062211:Spatio-Temporal Analysis; D014881:Water Supply / Q000706:statistics & numerical data*
keywords: risk factor