Multivariate outbreak detection

Linus Schiöler and Marianne Frisén

Journal of Applied Statistics, 2012, vol. 39, issue 2, pages 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
References: Add references at CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2011.584522 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Multivariate outbreak detection (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:taf:japsta:v:39:y:2012:i:2:p:223-242

Ordering information: This journal article can be ordered from
http://www.tandf.co.uk/journals/subscription.asp

Access Statistics for this article

Journal of Applied Statistics is edited by Professor Gopal K. Kanji

More articles in Journal of Applied Statistics from Taylor and Francis Journals
Series data maintained by Michael McNulty ().