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

Several epidemiologic assessments of the SARS-CoV have concluded that this coronavirus is sufficiently transmissible to cause a very large epidemic, but not so contagious as to be uncontrollable with good, basic public health measures. 3, 5 It is a common modelling practice to use a single or several separate data statistics to investigate individual epidemic properties and determinants, 6 and to quantify policies addressing problems caused by the disease. 34 Among other things, the spatial pattern, time process and driving forces of an epidemic can be explored by hierarchical clustering, SSEIR, and BW, respectively. Also, epidemiologic curves have been generated that display the day-to-day rate of SARS growth 19 but without accounting for the 'spatial pattern-time process-driving forces' associations of the epidemic. Only a limited number of studies have attempted to partially represent and evaluate determinant associations and links between separate statistics under conditions of uncertainty.