title: Multivariate modelling of infectious disease surveillance data.
abstract: This paper describes a model-based approach to analyse multivariate time series data on counts of infectious diseases. It extends a method previously described in the literature to deal with possible dependence between disease counts from different pathogens. In a spatio-temporal context it is proposed to include additional information on global dispersal of the pathogen in the model. Two examples are given: the first describes an analysis of weekly influenza and meningococcal disease counts from Germany. The second gives an analysis of the spatio-temporal spread of influenza in the U.S.A., 1996-2006, using air traffic information. Maximum likelihood estimates in this non-standard model class are obtained using general optimization routines, which are integrated in the R package surveillance.
authors.affiliation: Biostatistics Unit, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
authors.forename: M
authors.initials: M
authors.lastname: Paul
authors.forename: L
authors.initials: L
authors.lastname: Held
authors.forename: A M
authors.initials: AM
authors.lastname: Toschke
mesh_terms: D001699:Biometry / Q000379:methods*; D003141:Communicable Diseases / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D005858:Germany / Q000453:epidemiology; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology; D016013:Likelihood Functions; D008589:Meningococcal Infections / Q000453:epidemiology; D015233:Models, Statistical; D015999:Multivariate Analysis; D011159:Population Surveillance; D014481:United States / Q000453:epidemiology