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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。
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关键词:
- COVID-19流行 /
- 数据 /
- 一致性 /
- 周期性
Abstract: Using rigorous methods to identify and interpret the consistency and periodicity of multi-source COVID-19 epidemic data is the basis of properly interpreting the epidemic characteristics of the COVID-19 epidemic and developing prevention and control measures. By conducting a systematic review, we found that: (1) the existing literature on consistency studies mainly focused on global COVID-19 epidemic data released by the WHO and Johns Hopkins University, revealing inconsistent across various aspects of data collection, statistical indicators, data sharing, as well as the value of the same indicators; (2) each kind COVID-19 epidemic data had typical periodicity. Existing studies about periodicity of COVID-19 epidemic data were primarily from developed countries. The length of epidemic periodicity varied greatly across countries and regions, with a varying periodicity of 3.5 days, 5.0 days, 7.0 days, and 62.0 days. Researchers linked the periodicity of COVID-19 epidemic to multiple factors, including sample testing, data reporting, biological, social, and environmental factors. To summarize, there were no studies that quantified the consistency across major COVID-19 data sources, examined differences in data consistency over time and across countries, and explored the periodicity variations of COVID-19 epidemic across countries around the world currently.-
Key words:
- COVID-19 epidemic /
- Data /
- Consistency /
- Periodicity
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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 downloadCSV 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 downloadCSV 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 downloadCSV、XLSX、
JSON、XML官网下载
Download from official websiteOur 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 downloadCSV、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 resultAkdi, 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 daysKayode, et al[18] 2020年1―9月
January to September, 20207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 periodPavlícˇek, 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 cyclesBecker, et al [16] 2020年2―3月
February to March, 202012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 differentKundu, 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 periodBergman, 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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