Evidence map›Paper›PMID 34117851›Full record

ArticleZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2021

Impact of socioeconomic status,population mobility and control measures on COVID-10 development in major cities of China.

Sisi Wang, Yuanqing Ye, Xiaolin Xu, Sicong Wang, Xin Xu, Changzheng Yuan, Shu Li, Shuyin Cao, Wenyuan Li, Chen Chen and 5 more

Abstract read
In one paragraph

Article in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Sisi WangYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Yuanqing YeYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Xiaolin XuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Sicong WangYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Xin XuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Changzheng YuanYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Shu LiYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Shuyin CaoYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Wenyuan LiYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Chen ChenYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Kejia HuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Hao LeiYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Hui ZhuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Yong ZhuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.
Xifeng WuYale School of Public Health,Yale University,New Haven 06520,Connecticut,USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

:To evaluate the impact of socioeconomic status,population mobility,prevention and control measures on the early-stage coronavirus disease 2019 (COVID-19) development in major cities of China. : The rate of daily new confirmed COVID-19 cases in the 51 cities with the largest number of cumulative confirmed cases as of February 19,2020 (except those in Hubei province) were collected and analyzed using the time series cluster analysis. It was then assessed according to three aspects,that is, socioeconomic status,population mobility,and control measures for the pandemic. : According to the analysis on the 51 cities,4 development patterns of COVID-19 were obtained,including a high-incidence pattern (in Xinyu),a late high-incidence pattern (in Ganzi),a moderate incidence pattern (in Wenzhou and other 12 cities),and a low and stable incidence pattern (in Hangzhou and other 35 cities). Cities with different types and within the same type both had different scores on the three aspects. : There were relatively large difference on the COVID-19 development among different cities in China,possibly affected by socioeconomic status,population mobility and prevention and control measures that were taken. Therefore,a timely public health emergency response and travel restriction measures inside the city can interfere the development of the pandemic. Population flow from high risk area can largely affect the number of cumulative confirmed cases.

Indexed as

COVID-19ChinaCitiesHumansSARS-CoV-2Social ClassCluster analysisControl measuresCoronavirus disease 2019;ChinaPopulation flowsSocioeconomic status

Identifiers

PMID34117851
PMCPMC8675075

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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.