Epidemiological characteristics of COVID-19 in Gansu province/ 甘肃省新型冠状病毒肺炎流行病学特征分析

2020 
Objective To understand the epidemiological characteristics of COVID-19 cases in different epidemic stages in Gansu province Methods Epidemiological investigation was conducted to collect the information of confirmed COVID-19 cases, including demographic, epidemiological and clinical information Results As of 25 February 2020, a total of 91 confirmed COVID-19 cases had been reported in Gansu The epidemic of COVID-19 in Gansu can be divided as three different stages, i e imported case stage, imported-case plus indigenous case stage, and indigenous case stage A total of 63 cases were clustered cases (69 23%), 3 cases were medical staff infected with non-occupational exposure The initial symptoms included fever (54 95%, 50/91), cough (52 75%, 48/91), or fatigue (28 57%, 26/91), the proportion of each symptom showed a decreasing trend along with the three epidemic stages, but only the differences in proportions of fever (trend χ2=2 20, P<0 05) and fatigue (trend χ2=3 18, P<0 05) among the three epidemic stages were statistically significant The cases with critical severe symptoms accounted for 42 85% (6/14), 23 73% (14/59) and 16 67% (3/18), respectively, in three epidemic stages, showed a decreasing trend (H=6 45, P<0 05) Also, the incubation period prolonged along with the epidemic stage (F=51 65, P<0 01), but the intervals between disease onset and hospital visit (F=5 32, P<0 01), disease onset and diagnosis (F=5 25, P<0 01) became shorter along with the epidemic stage Additionally, the basic reproduction number (R0) had decreased from 2 61 in imported case stage to 0 66 in indigenous case stage Conclusions The COVID-19 epidemic in Gansu was caused by the imported cases, and about 2/3 cases were clustered ones No medical worker was observed to be infected by occupational exposure With the progression of COVID-19 epidemic in Gansu, the change in initial symptom and incubation period suggests, the early screening cannot only depend on body temperature monitoring
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