高瑞, 王琦琦, 向祥龙, 于石成, 谭枫. 2020年早期我国新型冠状病毒肺炎疫情轨迹分析[J]. 疾病监测, 2021, 36(11): 1117-1123. DOI: 10.3784/jbjc.202108160449
引用本文: 高瑞, 王琦琦, 向祥龙, 于石成, 谭枫. 2020年早期我国新型冠状病毒肺炎疫情轨迹分析[J]. 疾病监测, 2021, 36(11): 1117-1123. DOI: 10.3784/jbjc.202108160449
Gao Rui, Wang Qiqi, Xiang Xianglong, Yu Shicheng, Tan Feng. Trajectory of COVID-19 incidence in China in early 2020[J]. Disease Surveillance, 2021, 36(11): 1117-1123. DOI: 10.3784/jbjc.202108160449
Citation: Gao Rui, Wang Qiqi, Xiang Xianglong, Yu Shicheng, Tan Feng. Trajectory of COVID-19 incidence in China in early 2020[J]. Disease Surveillance, 2021, 36(11): 1117-1123. DOI: 10.3784/jbjc.202108160449

2020年早期我国新型冠状病毒肺炎疫情轨迹分析

Trajectory of COVID-19 incidence in China in early 2020

  • 摘要:
      目的   分析2020年早期全国31个省级行政区(不含香港、澳门特别行政区和台湾地区,下同)新型冠状病毒肺炎(COVID-19)发病数随时间的变化轨迹,探索早期疫情的发展规律和可能的影响因素。
      方法  从中国疾病预防控制信息系统传染病信息系统中获取截至2020年4月8日的COVID-19发病数据,应用SAS 9.4软件从不同区域层面拟合COVID-19发病数随时间变化的轨迹模型,依据其轨迹特征进行分组,并对不同亚组进行异质性分析,发掘其潜在差异。
      结果  在全国31个省级行政区层面上,COVID-19发病数随时间变化的轨迹可分为2个组,第1组和第2组为三次项曲线,呈“升–降–升”的趋势,湖北省属于第2组,其他地区属于第1组,其轨迹曲线的峰值远远低于第2组。在除湖北省以外的30个省级行政区层面上,分为2个组,均为三次项曲线,毗邻湖北省的几个省份、东北部分地区和东部几个沿海省份属于第2组,该组发病峰值较高,其余属于第1组。 在湖北省各地级市层面上,分为2个组,分别为二次和四次项曲线,武汉市为第2组,其余各市为第1组。 在武汉市各区层面上,分为2个组,分别为二次和四次项曲线。
      结论  我国不同地区COVID-19发病数随时间变化的轨迹存在异质性,疫情早发生地病例基数大,传播范围广、速度快。另外,COVID-19传播与相对地理位置、初始输入病例数等因素紧密相关。 利用轨迹模型研究疫情发展规律,有助于针对性地制定COVID-19防控策略,为新发呼吸道传染病提供一定防控经验。

     

    Abstract:
      Objective  To understand the changing trajectory of the incidence of COVID-19 over time in the early phase of COVID-19 epidemic in 31 provincial level administrative divisions in China, except Hong Kong, Macao, and Taiwan, and explore the development pattern and possible influencing factors of the epidemic.
      Methods  The incidence data of COVID-19 reported in China as of 8 April 2020 were obtained from the Infectious Disease Information System of Chinese Center for Disease Control and Prevention. The trajectory models of change of case numbers of COVID-19 over time at the four administrative levels were fitted by using software SAS 9.4, and the areas at different administrative levels were divided into different groups according to the trajectory characteristics, and the heterogeneity of different subgroups was analyzed to explore the potential difference.
      Results  At the level of 31 provincial administrative regions in China, the trajectory of the case numbers of COVID-19 over time could be divided into two groups. Group 1 and group 2 had a cubic incidence curve, showing an “upward-down-upward” trend. Hubei province belonged to group 2, and other provincial administrative regions belonged to group 1 with the peak value of the trajectory curve much lower than that of group 2. At the level of 30 provincial administrative divisions in China except Hubei, two groups were divided, with a cubic incidence curve. Several provinces adjacent to Hubei, parts of northeastern China and several coastal provinces in eastern China belonged to group 2, which had a higher peak incidence, and the rest belonged to group 1. At the prefecture-level in Hubei province, two groups were divided, which had quadratic or quartic were curves of incidences, respectively. Wuhan belonged to group 2, and the other areas belonged to group 1. At the level of districts/counties in Wuhan, two groups were divided, which had quadratic and quartic curves of incidence, respectively.
      Conclusion  The time-varying trajectories of the number of COVID-19 cases in different regions of China had heterogeneity, with a large number of cases, a wide range of transmission, and a fast transmission rate in the area affected by epidemic firstly. In addition, the spread of COVID-19 was closely related to geographical location, population density, travel intensity, and climatic environment. Using trajectory model to study the epidemic development trend is helpful to formulate targeted COVID-19 prevention and control strategies and provide reference for the prevention and control of emerging respiratory infectious diseases.

     

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