SPEAKER: Prof. Kwok Tsui

ABSTRACT:

For public health surveillance, timely detection of a rate increase in disease incidence is very important. This talks reviews some popular methods for temporal surveillance and proposes a general framework for spatial and spatiotemporal surveillance based onlikelihood ratio statistics over windows of tests. We show that the CUSUM and other popular likelihood ratio statistics are special cases under such a general framework. We compare the efficiency of these surveillance methods in spatial and spatiotemporal cases for detecting clusters of incidence using both Monte Carlo simulations and a real example.  We will also discuss the generalization of weighted likelihood ratio tests for detecting different shift magnitudes under homogeneous and non-homogeneous populations.