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

By means of the novel methodological framework considered in the present work, future epidemic studies may not only benefit from 'meta data analysis' but also from 'meta model analysis'. A wide variety of inter-disciplinary datasets that are relevant to the epidemic spread can be integrated by means of this framework. The statistics functions and spatial analysis techniques described above can be readily applied to the study of epidemics other than SARS. Furthermore, the methodological framework is not limited to the statistical tools and datasets considered in this paper, but is more general and flexible, in the sense that it may include additional information sources, statistics functions and models; and it can allow the performance of other types of logical inferences, thus generating new findings and conclusions concerning the epidemic under investigation. By generalizing the current approach within the context of random field theories with temporal GIS techniques, one may consider additional issues related to the next generation of (stochastic) SARS models allowing for space-time variations, multi-sourced uncertainty, seasonality and varying transmission modes, the case of SARS virus long-time persistence, and examining the case of global eradication rather than local control in terms of early epidemic detection measures.