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Multivariate modelling of infectious disease surveillance data
Author(s) -
Paul M.,
Held L.,
Toschke A. M.
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3440
Subject(s) - multivariate statistics , infectious disease (medical specialty) , context (archaeology) , multivariate analysis , covariate , computer science , r package , statistics , disease surveillance , maximum likelihood , econometrics , data mining , disease , geography , medicine , mathematics , machine learning , archaeology , pathology
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 . Copyright © 2008 John Wiley & Sons, Ltd.