Evidence map›Paper›PMID 35288642›Full record

ArticleScientific reports2022

Analysis on the characteristics of spatio-temporal evolution and aggregation trend of early COVID-19 in mainland China.

Shengxian Bi, Siyu Bie, Xijian Hu, Huiguo Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Shengxian Bi *Department of Mathematics and Systems Science, Xinjiang University, Urumqi, 830046, China.
Siyu Bie *Department of Mathematics and Systems Science, Xinjiang University, Urumqi, 830046, China.
Xijian HuDepartment of Mathematics and Systems Science, Xinjiang University, Urumqi, 830046, China. xijianhu@xju.edu.cn.
Huiguo ZhangDepartment of Mathematics and Systems Science, Xinjiang University, Urumqi, 830046, China.

Funding

National Natural Science Foundation of China 11961065Research Programs of Higher Education in Xinjiang Autonomous Region XJEDU2017M001
6 · The paper itself

Abstract

To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran's I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China's experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic.

Indexed as

COVID-19ChinaCluster AnalysisHumansIncidenceSpatio-Temporal Analysis

Identifiers

PMID35288642
PMCPMC8919916

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