Spatial outbreak detection based on inference principles for multivariate surveillance
Abstract: Spatial surveillance is a special case of multivariate
surveillance. Thus, in this review of spatial outbreak methods, the
relation to general multivariate surveillance approaches is discussed.
Different outbreak models are needed for different public health
applications. We will discuss methods for the detection of: 1) Spatial
clusters of increased incidence, 2) Increased incidence at only one
(unknown) location, 3) Simultaneous increase at all locations, 4) Outbreaks
with a time lag between the onsets in different regions. Spatial outbreaks
are characterized by the relation between the times of the onsets of the
outbreaks at different locations. The sufficient reduction plays an
important role in finding a likelihood ratio method. The change at the
outbreak may be a step change from the non-epidemic period to an increased
incidence level. However, errors in the estimation of the baseline have
great influence and nonparametric methods are of interest. For the seasonal
influenza in Sweden the outbreak was characterized by a monotonic increase
following the constant non-epidemic level. A semiparametric generalized
likelihood ratio surveillance method was used. Appropriate evaluation
metrics are important since they should agree with the aim of the
application. Evaluation in spatial and other multivariate surveillance
requires special concern.
Keywords: Monitoring; Influenza; Sufficiency; Semiparametric; Generalized likelihood; Timeliness; Predicted value; (follow links to similar papers)
JEL-Codes: C10; (follow links to similar papers)
20 pages, March 5, 2012
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