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深度学习和社交网络分析揭示了注射吸毒高发人群中艾滋病毒传播的驱动因素。

Deep learning and social network analysis elucidate drivers of HIV transmission in a high-incidence cohort of people who inject drugs.

作者信息

Clipman Steven J, Mehta Shruti H, Mohapatra Shobha, Srikrishnan Aylur K, Zook Katie J C, Duggal Priya, Saravanan Shanmugam, Nandagopal Paneerselvam, Kumar Muniratnam Suresh, Lucas Gregory M, Latkin Carl A, Solomon Sunil S

机构信息

Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

出版信息

Sci Adv. 2022 Oct 21;8(42):eabf0158. doi: 10.1126/sciadv.abf0158. Epub 2022 Oct 19.

DOI:10.1126/sciadv.abf0158
PMID:36260674
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9581475/
Abstract

Globally, people who inject drugs (PWID) experience some of the fastest-growing HIV epidemics. Network-based approaches represent a powerful tool for understanding and combating these epidemics; however, detailed social network studies are limited and pose analytical challenges. We collected longitudinal social (injection partners) and spatial (injection venues) network information from 2512 PWID in New Delhi, India. We leveraged network analysis and graph neural networks (GNNs) to uncover factors associated with HIV transmission and identify optimal intervention delivery points. Longitudinal HIV incidence was 21.3 per 100 person-years. Overlapping community detection using GNNs revealed seven communities, with HIV incidence concentrated within one community. The injection venue most strongly associated with incidence was found to overlap six of the seven communities, suggesting that an intervention deployed at this one location could reach the majority of the sample. These findings highlight the utility of network analysis and deep learning in HIV program design.

摘要

在全球范围内，注射毒品者（PWID）经历着一些增长最快的艾滋病毒疫情。基于网络的方法是理解和应对这些疫情的有力工具；然而，详细的社会网络研究有限且带来分析挑战。我们从印度新德里的2512名注射毒品者那里收集了纵向社会（注射伙伴）和空间（注射场所）网络信息。我们利用网络分析和图神经网络（GNN）来揭示与艾滋病毒传播相关的因素，并确定最佳干预交付点。纵向艾滋病毒发病率为每100人年21.3例。使用GNN进行的重叠社区检测揭示了七个社区，艾滋病毒发病率集中在一个社区内。发现与发病率最密切相关的注射场所与七个社区中的六个重叠，这表明在这一地点开展的干预措施可以覆盖大部分样本。这些发现凸显了网络分析和深度学习在艾滋病毒项目设计中的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/a83dab51d63f/sciadv.abf0158-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/8717eccf3b10/sciadv.abf0158-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/a05608db89df/sciadv.abf0158-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/b8ca172850ee/sciadv.abf0158-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/a83dab51d63f/sciadv.abf0158-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/8717eccf3b10/sciadv.abf0158-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/a05608db89df/sciadv.abf0158-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/b8ca172850ee/sciadv.abf0158-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bbd/9581475/a83dab51d63f/sciadv.abf0158-f4.jpg

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[2]
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引用本文的文献

[1]
Network-based strategies to combat HCV: Examining social and spatial drivers of transmission among PWID in New Delhi.

J Viral Hepat. 2024-9

[2]
Injection network drivers of HIV prevention service utilization among people who inject drugs: results of a community-based sociometric network cohort in New Delhi, India.

J Int AIDS Soc. 2024-4

[3]
Network centrality and HIV prevention service use among people who inject drugs: Findings from a sociometric network cohort in New Delhi, India.

Addiction. 2024-3

[4]
Incidence of HIV and hepatitis C virus among people who inject drugs, and associations with age and sex or gender: a global systematic review and meta-analysis.

Lancet Gastroenterol Hepatol. 2023-6

[5]
FAD: Fine-Grained Adversarial Detection by Perturbation Intensity Classification.

Entropy (Basel). 2023-2-11

本文引用的文献

[1]
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Role of direct and indirect social and spatial ties in the diffusion of HIV and HCV among people who inject drugs: a cross-sectional community-based network analysis in New Delhi, India.

Elife. 2021-8-3

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