Parallel surveillance, separate monitoring of each continuous series, has been widely used for multivariate surveillance; however, it has severe limitations. First, 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 that 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, 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. (2) proposed a sufficient reduction method used for monitoring a mean shift in multivariate series where observations are assumed independent. Also, the former allows for CBS while the latter does not. In this study, we further develop sufficient reduction methods by taking CWS and CBS into account. At each time point, data from p-dimensional multivariate series are used to calculate sufficient statistics derived from the likelihood ratio between out of control and in control states. The evaluation of this method is by simulation study, where bivariate series are generated daily from different sets of parameters (whether or not CWS and/or CBS are present) (3). Detection of a mean shift, which is 2, 3 or 4 times standard deviation of background data, is investigated. A EWMA chart is used to monitor the resultant series of sufficient statistics, and the conditional expected delays (CED) and false alarm rates (FAR) (4) from four methods are compared.