Evidence map›Paper›PMID 42473636›Full record

ArticleInfectious Disease Modelling2027

From giant components to communities: community-level insights for prioritizing interventions within large clusters of HIV-1 transmission networks.

Huanchang Yan, Hao Wu, Jiahang Wang, Shunming Li, Yefei Luo, Lingxuan Lai, Jingyang Hu, Zhenming Tian, Yuanli Rao, Jing Gu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2027. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Huanchang YanSchool of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Hao WuDepartment of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Jiahang WangDepartment of Network, Data and Information, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
Shunming LiDepartment of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Yefei LuoDepartment of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Lingxuan LaiSchool of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Jingyang HuDepartment of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Zhenming TianSchool of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Yuanli RaoSchool of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.
Jing GuDepartment of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China.
Shixing TangInstitute for Global Health, Dermatology Hospital of Southern Medical University, Guangzhou, 510091, China.
Yuantao HaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Zhigang HanDepartment of AIDS Control and Prevention, Guangzhou Center for Disease Control and Prevention, Guangzhou, 510440, China.
Yu LiuSchool of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large clusters in HIV-1 molecular networks contain a substantial proportion of people living with HIV and dominate local epidemics; however, their large size and complex structure pose a challenge for effective public health interventions. We developed an analytical framework to partition the large cluster into small groups for precise intervention. In the HIV-1 CRF07_BC molecular transmission network in Guangzhou, China (2008-2020), a giant component (681 members) was partitioned into 34 communities with dense internal and sparse external links. All 378 inter-community links involved high-centrality members from Community 1 (

Indexed as

Community detectionExponential random graph modelHIVMolecular epidemiologyMolecular surveillanceMolecular transmission network

Identifiers

PMID42473636
PMCPMC13380797

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.