Comparison of the spatiotemporal characteristics of the COVID-19 and SARS outbreaks in mainland China

Xi Zhang; Hua-Xiang Rao; Yuwan Wu; Yubei Huang; Hongji Dai

We used ArcGIS software v10.2.2 (ESRI Inc., Redlands, CA, USA) to depict the spatial distribution and perform global and local spatial autocorrelation analyses. We used Kulldorff's space-time scan statistical analysis to detect the space-time clusters of SARS and COVID-19 and to verify whether the geographic clustering was caused by random variation. Considering the relatively low incidence rate, we used the discrete Poisson probability model as the scanning statistical model. In Kulldorff's space-time scanning, the radius of the population coverage was used, and the maximum spatial scanning area was set to cover 10% of the risk population. The maximum temporal scanning window was set to cover 50% of the total research time. The scan window was increased gradually from 0 to the maximum, and the log-likelihood ratios (LLRs) were calculated for each window. The window with the maximum likelihood was defined as the most likely cluster area. Other clusters with statistically significant LLRs were defined as the secondary potential clusters. The LLR P-value was estimated through 99,999 Monte Carlo simulations. A P-value < 0.05 indicated a significantly high risk inside of the scan window and a potential high-risk cluster of the disease. The relative risk (RR) of the . CC-BY 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.