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利用手机数据揭示纳米比亚艾滋病疫情背后的风险流动网络。

Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia.

机构信息

Center for Biomedical Modeling, The Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, Paris, France.

出版信息

Nat Commun. 2021 May 14;12(1):2837. doi: 10.1038/s41467-021-23051-w.


DOI:10.1038/s41467-021-23051-w
PMID:33990578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8121904/
Abstract

Twenty-six million people are living with HIV in sub-Saharan Africa; epidemics are widely dispersed, due to high levels of mobility. However, global elimination strategies do not consider mobility. We use Call Detail Records from 9 billion calls/texts to model mobility in Namibia; we quantify the epidemic-level impact by using a mathematical framework based on spatial networks. We find complex networks of risk flows dispersed risk countrywide: increasing the risk of acquiring HIV in some areas, decreasing it in others. Overall, 40% of risk was mobility-driven. Networks contained multiple risk hubs. All constituencies (administrative units) imported and exported risk, to varying degrees. A few exported very high levels of risk: their residents infected many residents of other constituencies. Notably, prevalence in the constituency exporting the most risk was below average. Large-scale networks of mobility-driven risk flows underlie generalized HIV epidemics in sub-Saharan Africa. In order to eliminate HIV, it is likely to become increasingly important to implement innovative control strategies that focus on disrupting risk flows.

摘要

撒哈拉以南非洲地区有 2600 万人感染了艾滋病毒;由于高度的流动性,疫情广泛传播。然而,全球消除战略并未考虑到流动性。我们使用来自 90 亿个通话/短信的通话记录来模拟纳米比亚的流动情况;我们使用基于空间网络的数学框架来量化疫情层面的影响。我们发现,风险流动的复杂网络分散在全国范围内:一些地区感染艾滋病毒的风险增加,另一些地区则降低。总的来说,40%的风险是由流动驱动的。网络中存在多个风险中心。所有选区(行政单位)都以不同程度的输入和输出风险。有几个选区输出的风险非常高:它们的居民感染了其他选区的许多居民。值得注意的是,输出风险最多的选区的患病率低于平均水平。流动驱动的风险流的大规模网络是撒哈拉以南非洲地区普遍存在的艾滋病毒疫情的基础。为了消除艾滋病毒,实施以阻断风险流为重点的创新控制战略可能变得越来越重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/324379ad2d44/41467_2021_23051_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/af7c907a0db2/41467_2021_23051_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/f2fa46dff72e/41467_2021_23051_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/bfe7ba20d341/41467_2021_23051_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/a15e0dca2f90/41467_2021_23051_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/324379ad2d44/41467_2021_23051_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/af7c907a0db2/41467_2021_23051_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/f2fa46dff72e/41467_2021_23051_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/bfe7ba20d341/41467_2021_23051_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/a15e0dca2f90/41467_2021_23051_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/09ea/8121904/324379ad2d44/41467_2021_23051_Fig5_HTML.jpg

相似文献

[1]
Using mobile phone data to reveal risk flow networks underlying the HIV epidemic in Namibia.

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[2]
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[3]
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[4]
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[5]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Metrics of Mobility by Sex are Associated with HIV Incidence in Rural Kenya and Uganda.

AIDS Behav. 2025-5-6

[2]
Extreme inequities in access to HIV treatment in Malawi.

Nat Med. 2025-5

[3]
Optimizing the detection of emerging infections using mobility-based spatial sampling.

Int J Appl Earth Obs Geoinf. 2024-7

[4]
Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.

Curr HIV/AIDS Rep. 2023-12

[5]
The role of migration networks in the development of Botswana's generalized HIV epidemic.

Elife. 2023-9-4

[6]
Disparities in mobile phone ownership reflect inequities in access to healthcare.

PLOS Digit Health. 2023-7-6

[7]
Population genomics of diarrheagenic Escherichia coli uncovers high connectivity between urban and rural communities in Ecuador.

Infect Genet Evol. 2023-9

[8]
Population mobility and the development of Botswana's generalized HIV epidemic: a network analysis.

medRxiv. 2023-2-2

[9]
Use of mobile phone data in HIV epidemic control.

Lancet HIV. 2022-12

[10]
Predictors of migration in an HIV hyper-endemic rural South African community: evidence from a population-based cohort (2005-2017).

BMC Public Health. 2022-6-7

本文引用的文献

[1]
Mobility network models of COVID-19 explain inequities and inform reopening.

Nature. 2021-1

[2]
Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study.

Lancet Digit Health. 2020-10-28

[3]
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle.

Sci Adv. 2020-6-5

[4]
Mapping of HIV-1C Transmission Networks Reveals Extensive Spread of Viral Lineages Across Villages in Botswana Treatment-as-Prevention Trial.

J Infect Dis. 2020-11-15

[5]
Migration, hotspots, and dispersal of HIV infection in Rakai, Uganda.

Nat Commun. 2020-2-20

[6]
Quantifying HIV transmission flow between high-prevalence hotspots and surrounding communities: a population-based study in Rakai, Uganda.

Lancet HIV. 2020-1-14

[7]
Declines in HIV incidence among men and women in a South African population-based cohort.

Nat Commun. 2019-12-2

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Precision public health and HIV in Africa.

Lancet Infect Dis. 2019-10

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Universal Testing, Expanded Treatment, and Incidence of HIV Infection in Botswana.

N Engl J Med. 2019-7-18

[10]
HIV Testing and Treatment with the Use of a Community Health Approach in Rural Africa.

N Engl J Med. 2019-7-18

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