Evidence map›Paper›PMID 39816757›Full record

ArticleInfectious Disease Modelling2025

Social contact patterns and their impact on the transmission of respiratory pathogens in rural China.

Yuxia Liang, Qian You, Qianli Wang, Xiaohong Yang, Guangjie Zhong, Kaige Dong, Zeyao Zhao, Nuolan Liu, Xuemei Yan, Wanying Lu and 8 more

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2025. 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

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

4 citing papers in PubMed.

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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

18 authors.

Yuxia LiangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Qian YouShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Qianli WangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Xiaohong YangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Guangjie ZhongShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Kaige DongShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Zeyao ZhaoShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Nuolan LiuShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Xuemei YanShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Wanying LuShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Cheng PengShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Jiaxin ZhouShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Jiqun LinAnhua County Centre for Disease Control and Prevention, Yiyang, Hunan, People's Republic of China.
Maria LitvinovaDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Mark JitDepartment of Infectious Disease Epidemiology, London School for Hygiene and Tropical Medicine, Faculty of Public Health and Policy, London, United Kingdom.
Marco AjelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Hongjie YuShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.
Juanjuan ZhangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Social contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China. Methods: We conducted a pioneering study to quantify social contact patterns in Anhua County, Hunan Province, China, from June to October 2021, when there were minimal coronavirus disease-related restrictions in the area. Additionally, we simulated the epidemics under different assumptions regarding the relative transmission risks of various contact types (e.g., indoor versus outdoor, and physical versus non-physical). Results: Participants reported an average of 12.0 contacts per day (95% confidence interval: 11.3-12.6), with a significantly higher number of indoor contacts compared to outdoor contacts. The number of contacts was associated with various socio-demographic characteristics, including age, education level, income, household size, and travel patterns. Contact patterns were assortative by age and varied based on the type of contact (e.g., physical versus non-physical). The reproduction number, daily incidence, and infection attack rate of simulated epidemics were remarkably stable. Discussion: We found many intergenerational households and contacts that pose challenges in preventing and controlling infections among the elderly in rural China. Our study also underscores the importance of integrating various types of contact pattern data into epidemiological models and provides guidance to public health authorities and other major stakeholders in preparing and responding to infectious disease threats in rural China.

Indexed as

Infectious diseaseMathematical modelingRespiratory pathogensRural areasSocial contact

Identifiers

PMID39816757
PMCPMC11732678

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.