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全球主要COVID-19数据来源间一致性及自身周期性研究进展

刘涵 宗慧莹 胡国清

刘涵, 宗慧莹, 胡国清. 全球主要COVID-19数据来源间一致性及自身周期性研究进展[J]. 中华疾病控制杂志, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017
引用本文: 刘涵, 宗慧莹, 胡国清. 全球主要COVID-19数据来源间一致性及自身周期性研究进展[J]. 中华疾病控制杂志, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017
LIU Han, ZONG Huiying, HU Guoqing. Advances in consistency and periodicity across major global COVID-19 data sources[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017
Citation: LIU Han, ZONG Huiying, HU Guoqing. Advances in consistency and periodicity across major global COVID-19 data sources[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(1): 108-111. doi: 10.16462/j.cnki.zhjbkz.2024.01.017

全球主要COVID-19数据来源间一致性及自身周期性研究进展

doi: 10.16462/j.cnki.zhjbkz.2024.01.017
基金项目: 

国家社会科学基金重大项目 20&ZD120

详细信息
    通讯作者:

    胡国清,E-mail: huguoqing009@gmail.com

  • 中图分类号: R181

Advances in consistency and periodicity across major global COVID-19 data sources

Funds: 

The National Social Science Foundation of China 20&ZD120

More Information
  • 摘要: 科学识别和解释不同COVID-19数据间一致性和周期性特征是准确了解COVID-19流行规律与制定防控措施的基础。本研究通过系统检索和梳理相关研究文献发现:(1)现有一致性研究集中于WHO、约翰霍普金斯大学等机构发布的全球COVID-19疫情数据,而且不同数据来源在数据收集、统计指标与数据共享、同一指标数值等方面不尽相同;(2)COVID-19疫情数据存在明显的周期性,现有的相关研究集中在发达国家,不同国家与地区间的疫情周期长短存在较大差异,有3.5 d、5.0 d、7.0 d和62.0 d等不同模式。关于周期性产生原因目前有样本检测和数据报告、生物学因素、社会因素、环境因素多种解释。综合来看,当前缺乏不同COVID-19数据来源间一致性的定量研究,暂未有研究评估数据一致性在不同时间和不同国家间的差异,也缺乏研究探讨全球不同国家的COVID-19疫情周期性差异。
  • 表  1  不同COVID-19数据库比较

    Table  1.   Comparison of different COVID-19 databases

    数据库名称
    Name of database
    数据来源
    Data source
    更新频率
    Renewal frequency
    下载方式
    Download method
    下载格式
    Download format
    贮存位置
    Storage location
    世界卫生组织
    WHO
    成员国官方机构公布数据
    Official agencies of member states publish data
    不定时更新
    Irregular renewal
    自由下载
    Free download
    CSV 官网下载
    Download from official website
    约翰霍普金斯大学
    Johns Hopkins University
    卫生部门或医疗机构临床资料
    Clinical data from health departments or medical institutions
    实时更新
    Real-time update
    自由下载
    Free download
    CSV GitHub
    欧洲疾病预防和控制中心
    European Center for Disease
    Prevention and Control
    国家开放数据门户和机构发布
    National open data portal and institutional publication
    每日更新
    Daily update
    自由下载
    Free download
    CSV、XLSX、
    JSON、XML
    官网下载
    Download from official website
    Our World in Data 综合世界卫生组织等官方部门数据
    Integrated data from the World Health Organization and other official departments
    每日更新
    Daily update
    自由下载
    Free download
    CSV、XLSX、JSON GitHub
    Worldometer 政府的官方网站或社交媒体账户
    Official government websites or social media accounts
    实时更新
    Real-time update
    不提供下载
    No download
    注:“―”表明此处无相关信息,该数据库不提供下载,所以无下载格式和贮存位置。
    Note: "―" indicates that there is no relevant information here, the database does not provide a download, so there are no download format and storage locations.
    下载: 导出CSV

    表  2  COVID-19疫情数据自身周期性研究文献主要特征

    Table  2.   Main features of literatures on COVID-19 epidemic data cyclicality

    作者
    Author
    研究时间段
    Research period
    研究国家/地区
    Research countries/regions
    研究内容
    Research content
    主要结果
    Main result
    Akdi, et al [13] 2020年3―6月
    March to June 2020
    土耳其
    Turkey
    每日感染病例数的周期特征
    Periodic characteristics of the number of daily infections
    每日感染人数具有4.0 d、5.0 d和62.0 d的周期特征
    The number of infected people per day has the periodic characteristics of 4.0 days, 5.0 days and 62.0 days
    Kayode, et al[18] 2020年1―9月
    January to September, 2020
    7个欧洲、美洲国家
    7 European and American countries
    每日确诊病例数和死亡病例数周期性
    The periodicity of the number of new cases and deaths per day
    数据均存在7.0 d的周期,部分国家还存在3.4 d和3.5 d周期
    The data all have a 7.0 day period, and some countries still have a 3.4 day and 3.5 day period
    Pavlícˇek, et al[14] 2020年2―5月
    February to May, 2020
    欧洲、美洲、亚洲等部分国家
    Europe, America, Asia and other countries
    每日确诊病例数和死亡病例数的周期性
    The periodicity of the number of new cases and deaths per day
    大部分国家数据存在7.0 d周期,感染率和死亡率的周期变化几乎同步,此外还存在3.5 d、14.0 d的周期
    In most countries, there is a 7.0 day cycle, and the periodic changes of infection rate and mortality rate are almost synchronous. In addition, there are 3.5 day and 14.0 day cycles
    Becker, et al [16] 2020年2―3月
    February to March, 2020
    12个北美、欧洲国家
    12 North American and European countries
    每日确诊病例数和死亡病例数的周期性
    The periodicity of the number of new cases and deaths per day
    数据均存在7.0 d的波动周期,不同国家的峰值存在差异
    The data all have a 7.0 day fluctuation period, and the peak values in different countries are different
    Kundu, et al[17] 2020年3―6月
    March to June, 2020
    全球、7个欧洲和美洲国家
    Global, 7 European and American countries
    每日感染人数和死亡人数的周期性
    The periodicity of the daily number of infections and deaths
    数据具有明显的7.0 d周期
    The data has an obvious 7.0 day period
    Bergman, et al[15] 2020年1―6月
    January to June, 2020
    纽约、洛杉矶New York, Los Angeles 发病率和死亡率数据的周期性Periodicity of morbidity and mortality data 发病率和死亡率存在高度一致的7.0 d变化周期There is a highly consistent 7.0 day change cycle between morbidity and mortality
    Huang, et al[19] 2020年1月―2021年5月
    January 2020 to May 2021
    南北半球
    Northern and southern hemispheres
    每日确诊病例数和死亡病例数据的周期性
    The periodicity of the number of new cases and deaths per day
    发现数据存在明显的以周为单位的周期特征和季节性特征
    It is found that the data has obvious periodic and seasonal characteristics in weeks
    耿雪倩, 等[20]
    Geng Xueqian,
    et al[20]
    2020年1月―2021年1月
    January 2020 to January 2021
    中国30个省份
    30 provinces in China
    发病率、死亡率的周期性
    Periodicity of morbidity and mortality data
    疫情发生存在季节性,但未发现7.0 d周期
    The outbreak was seasonal, but no 7.0 day cycle was found
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-12-28
  • 修回日期:  2023-03-20
  • 网络出版日期:  2024-02-05
  • 刊出日期:  2024-01-10

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