Epidemiological and clinical characteristics of COVID-19 in Shenzhen, the largest migrant city of China

Ying Wen; Lan Wei; Yuan Li; Xiujuan Tang; Shuo Feng; Kathy Leung; Xiaoliang Wu; Xiong-Fei Pan; Cong Chen; Junjie Xia; Xuan Zou; Tiejian Feng; Shujiang Mei

Demographic and clinical characteristics as categorical variables were presented as numbers and percentages, while continuous variables were presented as means and standard deviations or 95% confidence intervals (CI), or medians and interquartile ranges (IQR) if appropriate. Inter-group differences in the characteristics were tested by using Pearson's χ 2 test or Fisher's exact test for categorical variables, and by using Student's t-test or analysis of variance for continuous variables showing a normal distribution, and Kruskal-Wallis and Wilcoxon tests for continuous variables with non-parametric distribution. The incubation period was estimated by using a previously described parametric accelerated failure time model (10). Patients with detailed information on the time of exposure, the date of illness onset, or the first time of presentation were included for this analysis. We fitted lognormal, gamma, and Weilbull distributions using Markov Chain Monto Carlo in a Bayesian framework (11). We estimated the serial interval by using the time difference of illness onset between the infector and infectee. Initial reproductive number was estimated by using the best fit model based on date of illness onset of the early (Jan 10-23) local exposed cases without relation to the imported cases and the estimated serial interval of COVID-19. Logistic regression models were applied to identify factors associated with the clinical severity of COVID-19. All statistical tests and analyses of the incubation period, serial interval, and initial reproductive number were performed in R software (R foundation for Statistical Computing).