Agent-based network model predicts strong benefits to youth-centered HIV treatment-as-prevention efforts Article Swipe
John E. Mittler
,
James T. Murphy
,
Sarah E. Stansfield
,
Kathryn Peebles
,
Geoffrey S. Gottlieb
,
Steven M. Goodreau
,
Joshua T. Herbeck
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/207126
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1101/207126
We used an agent-based network model to examine the effect of targeting different risk groups with unsuppressed HIV viral load for linkage or re-linkage to HIV-related treatment services in a heterosexual population with annual testing. Our model identifies prevention strategies that can reduce incidence to negligible levels (i.e., less than 0.1 infections per 100 person-years) 20 years after a targeted Treatment-as-Prevention (TasP) campaign. The model assumes that most (default 95%) of the population is reachable (i.e., could, in principle, be linked to effective care) and a modest (default 5% per year) probability of a treated person dropping out of care. Under random allocation or CD4-based targeting, the default version of our model predicts that the TasP campaign would need to suppress viral replication in ~80% of infected people to halt the epidemic. Under age-based strategies, by contrast, this percentage drops to 50% to 60% (for strategies targeting those <30 and <25, respectively). Age-based targeting did not need to be highly exclusive to yield significant benefits; e.g. the scenario that targeted those <25 years old saw ~80% of suppressed individuals fall outside the target group. This advantage to youth-based targeting remained in sensitivity analyses in which key age-related risk factors were eliminated one by one. As testing rates increase in response to UNAIDS 90-90-90 goals, we suggest that efforts to link all young people to effective care could be an effective long-term method for ending the HIV epidemic. Linking greater numbers of young people to effective care will be critical for developing countries in which a demographic “youth bulge” is starting to increase the number of young people at risk for HIV infection.
Related Topics
Concepts
Treatment as prevention
Population
Human immunodeficiency virus (HIV)
Linkage (software)
Medicine
Demography
Environmental health
Antiretroviral therapy
Viral load
Immunology
Biology
Genetics
Gene
Sociology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/207126
- https://www.biorxiv.org/content/biorxiv/early/2017/10/21/207126.full.pdf
- OA Status
- green
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2766183792
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2766183792Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/207126Digital Object Identifier
- Title
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Agent-based network model predicts strong benefits to youth-centered HIV treatment-as-prevention effortsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-10-21Full publication date if available
- Authors
-
John E. Mittler, James T. Murphy, Sarah E. Stansfield, Kathryn Peebles, Geoffrey S. Gottlieb, Steven M. Goodreau, Joshua T. HerbeckList of authors in order
- Landing page
-
https://doi.org/10.1101/207126Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2017/10/21/207126.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2017/10/21/207126.full.pdfDirect OA link when available
- Concepts
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Treatment as prevention, Population, Human immunodeficiency virus (HIV), Linkage (software), Medicine, Demography, Environmental health, Antiretroviral therapy, Viral load, Immunology, Biology, Genetics, Gene, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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