Evidence map›Paper›PMID 35167771›Full record

ArticleJournal of the Royal Society, Interface2022

The spatial dissemination of COVID-19 and associated socio-economic consequences.

Yafei Zhang, Lin Wang, Jonathan J H Zhu, Xiaofan Wang

Open access · hybridAbstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact, top 100% of its field
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

3 citing papers in PubMed, 0 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 3 institutions in 2 countries.

Yafei ZhangDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.ORCID 0000-0002-3109-8077
Lin WangDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
Jonathan J H ZhuDepartment of Media and Communication, and School of Data Science, City University of Hong Kong, Hong Kong S.A.R., People's Republic of China.ORCID 0000-0001-6173-6941
Xiaofan WangDepartment of Automation, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
Ministry of Education of the People's Republic of China · CNCity University of Hong Kong · HKShanghai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.

Indexed as

COVID-19HumansPandemicsSARS-CoV-2Socioeconomic FactorsTravelcomplex networkCOVID-19diffusionhuman mobility

Identifiers

PMID35167771
PMCPMC8847004
OpenAlexW3179211961

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.