In this study, we compare two methods of generating grid points to enable efficient geographic cluster detection when the original geographical data are prohibitively numerous. One method generates uniform grid points, and the other employs quad trees to generate non-uniform grid points. We observe differences in the results of the spatial scan approach to cluster detection for both of these grid generation schemes. In both our simulated experiment, and our analysis of real data, the grid generation schemes produce different results. Generally speaking, the quad tree scheme is more sensitive to detecting high resolution spatial clusters than the uniform scheme. The quad tree grid point scheme may be a useful alternative to the uniform (and other) grid point generation schemes when it is important to set up a surveillance system sensitive to clusters at unspecified spatial resolutions. The quad tree grid scheme may also be useful in a number of other geographic surveillance applications.
Svenson Lawrence
Standardized electronic pre-diagnostic information is routinely collected in Alberta, Canada. ARTSSN is an automated real-time surveillance data repository able to rapidly refresh data that include school absenteeism information, calls about health concerns from Health Link Alberta; a provincial telephone service for health advice and information, and emergency department visits categorized by standardized chief complaint. Until recently, real-time ARTSSN data for public health surveillance and decision making has been underutilized.
Objective
We developed early warning algorithms using data from ARTSSN and used them to detect signatures of potential pandemics and provide regular weekly forecasts on influenza trends in Alberta during 2012-2014.