Parallel surveillance, separate monitoring of each continuous series, has been widely used for multivariate surveillance, however, it has severe limitations. Firstly, it faces the problem of multiplicity from multiple testing. Also, the ignorance of CBS reduces the performance of outbreak detection if data are truly correlated. Finally, since health data are normally dependent over time, CWS is another issue which should be taken into account. Sufficient reduction methods are used to reduce the dimensionality of a simple multivariate series to a univariate series which has been proved to be sufficient for monitoring a mean shift in multivariate surveillance (1 and 2). Having considered the sufficiency property and the nature of health data, we propose a sufficient reduction method for detecting a mean shift in multivariate series where CWS and CBS are taken into account.
Objective
To reduce the dimensionality of p-dimensional multivariate series to a univariate series by deriving sufficient statistics which take into account all the information in the original data, correlation within series (CWS) and correlation between series (CBS).