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

The present work is the first systematic effort to represent the aforementioned associations and links in a rigorous manner and to assess their considerable role in understanding the various aspects of epidemic distribution across space-time. Advanced mathematical modelling of an epidemic could potentially unveil the relationships between various determinants, but this modelling is often impossible due to the unavailability of the necessary scientific knowledge, including a lack of understanding of the fundamental disease mechanisms. 35, 36 Nevertheless, the present study shows that using the available datasets an informative modelling approach can be developed that is based on the pairwise linking of epidemic determinants and the formulation of a network that systematically connects the relevant statistics. In addition, unlike earlier SARS modelling studies, 5 the present approach accounts in an efficient manner for the spatial variation of the data available. This approach is particularly useful when the disease mechanisms are not clear and the corresponding mathematical model is difficult to develop for reasons mentioned above. Moreover, the approach can be successfully implemented to simulate the response of SARS transmission to various epidemic control factors, identify target areas and determine the critical time and relevant factors. Given the response by individuals, institutions and governments to a new and dangerous disease, the findings of the present study can be useful in the implementation of efficient epidemic intervention strategies and effective population protection policies.