Multivariate outbreak detection
AbstractOn-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 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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Bibliographic InfoPaper provided by Statistical Research Unit, Department of Economics, School of Business, Economics and Law, University of Gothenburg in its series Research Reports with number 2010:2.
Length: 22 pages
Date of creation: 17 Sep 2010
Date of revision:
Publication status: Forthcoming as Schiöler, Linus and Marianne Frisén, 'Multivariate outbreak detection' in Journal of Applied Statistics, 2011.
Contact details of provider:
Postal: Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG
Web page: http://www.statistics.gu.se/
Exponential family; Generalised likelihood; Ordered regression; Regional data; Surveillance;
Other versions of this item:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-09-25 (All new papers)
- NEP-ECM-2010-09-25 (Econometrics)
- NEP-HEA-2010-09-25 (Health Economics)
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