Data-driven exploration of ‘spatial pattern-time process-driving forces’ associations of SARS epidemic in Beijing, China

Wang, Jin-Feng; Christakos, George; Han, Wei-Guo; Meng, Bin

In general, the evolution of an epidemic is represented in terms of some kind of a mechanistic model. 7 -14 However, in the case of SARS (as well as in many other epidemic situations) only datasets of various kinds are available, whereas little is known about the underlying disease mechanisms. Mainstream statistics, time series, meta-analysis, randomized control trials and spatial techniques have been used to detect disease characteristics in a given dataset, 15 -18 but the substantive relationships between the disease characteristics across space-time often remain unknown. Similarly, the procedure generating epidemiologic curves 19 (i.e. curves showing the day-to-day rate of SARS growth) does not account for important associations between epidemic characteristics and dominant space-time disease patterns and dependencies. Despite the aforementioned limitations of the mainstream approaches, it is highly desirable to develop methods that can incorporate the scattered and incomplete datasets generated by various surveillance systems 20 -22 and by different programmatic and sectoral actions 23 in order to evaluate salient associations between epidemic determinants and quantify consequential space-time variations and patterns.