Evidence map›Paper›PMID 41290138›Full record

ArticleJournal of the Royal Society, Interface2025

First- and second-order social contact network structure in southern China.

Claire Perrin Smith, Jonathan Michael Read, Steven Riley, Derek Cummings, Kin On Kwok, Chao Qiang Jiang, Justin Lessler

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Claire Perrin SmithDepartment of Epidemiology, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA.ORCID 0000-0003-1069-9460
Jonathan Michael ReadDepartment of Lancaster Medical School, Lancaster University, Lancaster, UK.ORCID 0000-0002-9697-0962
Steven RileyMRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Derek CummingsDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Kin On KwokJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong.ORCID 0000-0002-2804-5433
Chao Qiang JiangGuangzhou Twelfth People's Hospital, Guangzhou, Guangdong, People's Republic of China.
Justin LesslerDepartment of Epidemiology, The University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA.

Funding

Center for Forecasting and Outbreak AnalyticsFIC NIH HHSHealth and Medical Research FundMedical Research CouncilNIH HHSWellcome Trust
6 · The paper itself

Abstract

The contact network structure resulting from social interaction between people is a key aspect of epidemic dynamics and control. While many studies have measured first-order network characteristics such as degree, measuring higher order properties of these networks, such as clustering, remains a challenge. Here, we present the results of a study of first- and second-order network structure from a representative cohort of individuals in Guangdong province, China. The number of reported daily contacts is similar across individuals aged 2 to 55 years, except for young adults (ages 16-25) who have relatively fewer daily contacts, while the number of contacts declines with age above 55 years old. The association between age and contact rate persisted after adjusting for mediating factors. Individuals living in higher population density areas made more contacts outside the home than individuals in low-density areas. Contacts of young children and older adults were more locally clustered than middle-aged adults. Individuals living in high population density areas had lower levels of local clustering compared with individuals from low-density areas. Adjustment for characteristics of the contacts themselves reduces the variation in local clustering between participants of different ages; however, the strong association with population density remains.

Indexed as

Population DensityAdultChildChinaCOVID-19HumansMaleSocial Networkingclusteringcontactnetwork structuretransmission

Identifiers

PMID41290138
PMCPMC12646775

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.