Evidence map›Paper›PMID 33648606›Full record

ArticleInfectious diseases of poverty2021

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

Ming-Fan Pang, Zuo-Ru Liang, Zhi-Da Cheng, Xin-Ping Yang, Jie-Wen Wu, Ke Lyu, Jing-Jing Xi, Zhong-Jie Li, Guo-Qing Shi, Yan-Ping Zhang and 3 more

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Evolving trend change during the COVID-19 pandemic.Frontiers in public health · 2022
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ming-Fan Pang *Center for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Zuo-Ru Liang *Center for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Zhi-Da ChengYidu Cloud (Beijing) Technology Co., Ltd., Beijing, China.
Xin-Ping YangCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Jie-Wen WuCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Ke LyuCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Jing-Jing XiCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Zhong-Jie LiCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Guo-Qing ShiCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Yan-Ping ZhangCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
George F GaoCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China.
Xiao-Peng QiCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China. qixp@chinacdc.cn.ORCID http://orcid.org/0000-0003-1145-3219
Xiao-Ping DongCenter for Global Public Health, Chinese Center for Disease Control and Prevention, Changping District, Beijing, 102206, China. dongxp@chinacdc.cn.

Funding

China-Africa Cooperation Program on Emerging and Re-emerging Infectious Diseases Control 2020C400032Key Technologies Research and Development Program 2020YFC0846300
6 · The paper itself

Abstract

backgroundConsidering 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.

methodsUsing 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.

resultsFrom 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%.

conclusionsWe 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.

Indexed as

Computer GraphicsCOVID-19Global HealthHumansPandemicsPublic Health SurveillanceSpatio-Temporal AnalysisBalloon chartCOVID-19GlobalSpatiotemporal visualizationSurveillance

Identifiers

PMID33648606
PMCPMC7919986

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.