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Multivariate outbreak detection

Journal article
Authors Linus Schiöler
Marianne Frisén
Published in Journal of Applied Statistics
Volume 39
Issue 2
Pages 223-242
ISSN 0266-4763
Publication year 2012
Published at Department of Economics, Statistical Research Unit
Pages 223-242
Language en
Links dx.doi.org/10.1080/02664763.2011.58...
https://gup.ub.gu.se/file/83218
Keywords exponential family, generalised likelihood, ordered regression, spatial data, surveillance
Subject categories Statistics, Biostatistics

Abstract

On-line monitoring is needed to detect outbreaks of diseases like influenza. Surveillance is also needed for other kinds of outbreaks, in the sense of an increasing expected value after a constant period. Information on spatial location or other variables might be available and may be utilized. We adapted a robust method for outbreak detection to a multivariate case. The relation between the times of the onsets of the outbreaks at different locations (or some other variable) was used to determine the sufficient statistic for surveillance. The derived maximum likelihood estimator of the outbreak regression was semi-parametric in the sense that the baseline and the slope were non-parametric while the distribution belonged to the one-parameter exponential family. The estimator was used in a generalized likelihood ratio surveillance method. The method was evaluated with respect to robustness and efficiency in a simulation study and applied to spatial data for detection of influenza outbreaks in Sweden.

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