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气球图进行全球 COVID-19 监测的时空可视化。

Spatiotemporal visualization for the global COVID-19 surveillance by balloon chart.

机构信息

Center for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.

Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, China.

出版信息

Infect Dis Poverty. 2021 Mar 1;10(1):21. doi: 10.1186/s40249-021-00800-z.

DOI:10.1186/s40249-021-00800-z
PMID:33648606
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7919986/
Abstract

BACKGROUND

Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries.

METHODS

Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization.

RESULTS

From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%.

CONCLUSIONS

We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.

摘要

背景

考虑到 2019 年冠状病毒病(COVID-19)大流行在世界范围内的广泛传播,了解大流行的时空发展非常重要。在本研究中,我们旨在根据各大洲和国家的情况,直观显示 COVID-19 的时间相关变化。

方法

使用约翰霍普金斯大学(Johns Hopkins University)提供的 2020 年 2 月至 12 月的 COVID-19 病例和死亡数据,我们生成了带有多个流行病学指标的时间关联气球图,包括粗病死率(CFR)、发病率、死亡率和总病例数,以便在特定时间段内比较各地区和国家的大流行进展情况,综合了七个相关维度。面积图用于补充随时间推移在联合国地理区域内每日新增 COVID-19 病例变化的气球图显示。Javascript 和 Vega-Lite 被选来在浏览器中编程和映射 COVID-19 数据以进行可视化。

结果

从 2020 年 2 月 1 日至 12 月 20 日,COVID-19 大流行按时间顺序在联合国分区内传播。它首先在东亚地区报告,然后在欧洲(南、西和北)、北美、东欧和西亚、中美洲和南美洲、南部非洲、加勒比地区、南亚、北非、东南亚和大洋洲引起了几波疫情。自 10 月以来,欧洲、北美和西亚的气球迅速上升,到 12 月达到 200 至 500/10000 之间的高发病率水平,表明 COVID-19 出现了冬季新一波疫情,比之前的疫情规模更大。截至 2020 年 12 月下旬,一些欧洲和美洲国家的死亡率高得惊人,高达或超过 100/100000,代表国家有比利时、捷克共和国、西班牙、法国、意大利、英国、匈牙利、保加利亚、秘鲁、美国、阿根廷、巴西、智利和墨西哥。亚洲地区伊朗的死亡率最高(超过 60/100000),非洲地区南非的死亡率最高(40/100000)。在过去的 15 天里,大多数国家的 CFR 处于不到 5%的低水平,而墨西哥的 CFR 接近 10%,异常高。

结论

我们创造性地使用了可视化方法,综合了 7 个维度的流行病学和时空指标,从传染性和严重程度方面评估 COVID-19 大流行的进展情况。这种方法使公共卫生工作者和政策制定者能够灵活地比较和了解疫情。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/cf9c2574ade6/40249_2021_800_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/91bccfd410a8/40249_2021_800_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/bdf9e8054f3a/40249_2021_800_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/cf9c2574ade6/40249_2021_800_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/91bccfd410a8/40249_2021_800_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/bdf9e8054f3a/40249_2021_800_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c99b/7923471/cf9c2574ade6/40249_2021_800_Fig3_HTML.jpg

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