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An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic

Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges throu...

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Autores principales: Howison, Mark, Gillani, Fizza S., Novitsky, Vlad, Steingrimsson, Jon A., Fulton, John, Bertrand, Thomas, Howe, Katharine, Civitarese, Anna, Bhattarai, Lila, MacAskill, Meghan, Ronquillo, Guillermo, Hague, Joel, Dunn, Casey W., Bandy, Utpala, Hogan, Joseph W., Kantor, Rami
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058263/
https://www.ncbi.nlm.nih.gov/pubmed/36992446
http://dx.doi.org/10.3390/v15030737
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author Howison, Mark
Gillani, Fizza S.
Novitsky, Vlad
Steingrimsson, Jon A.
Fulton, John
Bertrand, Thomas
Howe, Katharine
Civitarese, Anna
Bhattarai, Lila
MacAskill, Meghan
Ronquillo, Guillermo
Hague, Joel
Dunn, Casey W.
Bandy, Utpala
Hogan, Joseph W.
Kantor, Rami
author_facet Howison, Mark
Gillani, Fizza S.
Novitsky, Vlad
Steingrimsson, Jon A.
Fulton, John
Bertrand, Thomas
Howe, Katharine
Civitarese, Anna
Bhattarai, Lila
MacAskill, Meghan
Ronquillo, Guillermo
Hague, Joel
Dunn, Casey W.
Bandy, Utpala
Hogan, Joseph W.
Kantor, Rami
author_sort Howison, Mark
collection PubMed
description Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission.
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spelling pubmed-100582632023-03-30 An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic Howison, Mark Gillani, Fizza S. Novitsky, Vlad Steingrimsson, Jon A. Fulton, John Bertrand, Thomas Howe, Katharine Civitarese, Anna Bhattarai, Lila MacAskill, Meghan Ronquillo, Guillermo Hague, Joel Dunn, Casey W. Bandy, Utpala Hogan, Joseph W. Kantor, Rami Viruses Article Molecular HIV cluster data can guide public health responses towards ending the HIV epidemic. Currently, real-time data integration, analysis, and interpretation are challenging, leading to a delayed public health response. We present a comprehensive methodology for addressing these challenges through data integration, analysis, and reporting. We integrated heterogeneous data sources across systems and developed an open-source, automatic bioinformatics pipeline that provides molecular HIV cluster data to inform public health responses to new statewide HIV-1 diagnoses, overcoming data management, computational, and analytical challenges. We demonstrate implementation of this pipeline in a statewide HIV epidemic and use it to compare the impact of specific phylogenetic and distance-only methods and datasets on molecular HIV cluster analyses. The pipeline was applied to 18 monthly datasets generated between January 2020 and June 2022 in Rhode Island, USA, that provide statewide molecular HIV data to support routine public health case management by a multi-disciplinary team. The resulting cluster analyses and near-real-time reporting guided public health actions in 37 phylogenetically clustered cases out of 57 new HIV-1 diagnoses. Of the 37, only 21 (57%) clustered by distance-only methods. Through a unique academic-public health partnership, an automated open-source pipeline was developed and applied to prospective, routine analysis of statewide molecular HIV data in near-real-time. This collaboration informed public health actions to optimize disruption of HIV transmission. MDPI 2023-03-13 /pmc/articles/PMC10058263/ /pubmed/36992446 http://dx.doi.org/10.3390/v15030737 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Howison, Mark
Gillani, Fizza S.
Novitsky, Vlad
Steingrimsson, Jon A.
Fulton, John
Bertrand, Thomas
Howe, Katharine
Civitarese, Anna
Bhattarai, Lila
MacAskill, Meghan
Ronquillo, Guillermo
Hague, Joel
Dunn, Casey W.
Bandy, Utpala
Hogan, Joseph W.
Kantor, Rami
An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title_full An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title_fullStr An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title_full_unstemmed An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title_short An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic
title_sort automated bioinformatics pipeline informing near-real-time public health responses to new hiv diagnoses in a statewide hiv epidemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058263/
https://www.ncbi.nlm.nih.gov/pubmed/36992446
http://dx.doi.org/10.3390/v15030737
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