Evidence map›Paper›PMID 37521201›Full record

Articlenpj urban sustainability2023

A general urban spreading pattern of COVID-19 and its underlying mechanism.

Hongshen Zhang, Yongtao Zhang, Shibo He, Yi Fang, Yanggang Cheng, Zhiguo Shi, Cunqi Shao, Chao Li, Songmin Ying, Zhenyu Gong and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj urban sustainability, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. HIV Transmission Dynamics in Greater Mexico City are Shaped by Dense Spatial Mixing.medRxiv : the preprint server for health sciences · 2026
    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

16 authors.

Hongshen ZhangCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Yongtao ZhangCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Shibo HeCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.ORCID 0000-0002-1505-6766
Yi FangWestlake Institute for Data Intelligence, Hangzhou, China.
Yanggang ChengCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Zhiguo ShiCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China.
Cunqi ShaoCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Chao LiCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Songmin YingSchool of Medicine, Zhejiang University, Hangzhou, China.
Zhenyu GongZhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
Yu LiuWestlake Institute for Data Intelligence, Hangzhou, China.ORCID 0000-0002-5228-6050
Lin DongWestlake Institute for Data Intelligence, Hangzhou, China.ORCID 0000-0001-8433-6297
Youxian SunCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Jianmin JiaShenzhen Finance Institute, School of Management and Economics, The Chinese University of Hong Kong, Shenzhen, China.
H Eugene StanleyCenter for Polymer Studies and Physics Department, Boston University, Boston, MA 02215 USA.
Jiming ChenCollege of Control Science and Engineering, Zhejiang University, Hangzhou, China.ORCID 0000-0003-3155-3145

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.

Indexed as

DiseasesSocial sciences

Identifiers

PMID37521201
PMCPMC9883831

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.