Multivariate outbreak detection

Linus Schiöler and Marianne Fris�n
Authors registered in the RePEc Author Service: Marianne Frisén

Journal of Applied Statistics, 2012, vol. 39, issue 2, 223-242

Abstract: Online monitoring is needed to detect outbreaks of diseases such as 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.

Date: 2012
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Citations: View citations in EconPapers (2)

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Working Paper: Multivariate outbreak detection (2010)
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DOI: 10.1080/02664763.2011.584522

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