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

Temporal changes in the spatial clustering of cases, C(t), were determined by the time-variation of Moran's I M coefficient. The I M -curve was then filtered using a discrete wavelet transform implemented by MatLab (with Daubechies db3 as mother wave and four decomposition levels). In this way, the curve was decomposed into a low-frequency component (a 4 ) and high-frequency components (d 1 , d 2 , d 3 , d 4 ) (Fig. 2) . Daily data on new SARS cases were grouped according to the district of residence. The time-variation of a 4 indicated that SARS cases in Beijing became increasingly clustered Seven suspect determinants of the space-time SARS transmission, jointly denoted by F(t), were investigated by means of the BW join-count test. 33 The spatial proximity factor is denoted by T(t). Accordingly, Fig. 3 displays the determinants associated with SARS transmission dynamics.