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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Viruses |
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| Online Access: | https://www.mdpi.com/1999-4915/15/3/737 |
| _version_ | 1797608639186337792 |
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| author | Mark Howison Fizza S. Gillani Vlad Novitsky Jon A. Steingrimsson John Fulton Thomas Bertrand Katharine Howe Anna Civitarese Lila Bhattarai Meghan MacAskill Guillermo Ronquillo Joel Hague Casey W. Dunn Utpala Bandy Joseph W. Hogan Rami Kantor |
| author_facet | Mark Howison Fizza S. Gillani Vlad Novitsky Jon A. Steingrimsson John Fulton Thomas Bertrand Katharine Howe Anna Civitarese Lila Bhattarai Meghan MacAskill Guillermo Ronquillo Joel Hague Casey W. Dunn Utpala Bandy Joseph W. Hogan Rami Kantor |
| author_sort | Mark Howison |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T05:46:02Z |
| format | Article |
| id | doaj.art-a74cd0322ca94271af5ec0666b8eff41 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-11T05:46:02Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-a74cd0322ca94271af5ec0666b8eff412023-11-17T14:23:38ZengMDPI AGViruses1999-49152023-03-0115373710.3390/v15030737An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV EpidemicMark Howison0Fizza S. Gillani1Vlad Novitsky2Jon A. Steingrimsson3John Fulton4Thomas Bertrand5Katharine Howe6Anna Civitarese7Lila Bhattarai8Meghan MacAskill9Guillermo Ronquillo10Joel Hague11Casey W. Dunn12Utpala Bandy13Joseph W. Hogan14Rami Kantor15Research Improving People’s Lives, Providence, RI 02903, USADepartment of Medicine, Brown University, Providence, RI 02906, USADepartment of Medicine, Brown University, Providence, RI 02906, USADepartment of Biostatistics, Brown University School of Public Health, Providence, RI 02903, USADepartment of Behavioral and Social Sciences, Brown University, Providence, RI 02903, USARhode Island Department of Health, Providence, RI 02908, USARhode Island Department of Health, Providence, RI 02908, USARhode Island Department of Health, Providence, RI 02908, USARhode Island Department of Health, Providence, RI 02908, USARhode Island Department of Health, Providence, RI 02908, USARhode Island Department of Health, Providence, RI 02908, USADepartment of Medicine, Brown University, Providence, RI 02906, USADepartment of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06511, USARhode Island Department of Health, Providence, RI 02908, USADepartment of Biostatistics, Brown University School of Public Health, Providence, RI 02903, USADepartment of Medicine, Brown University, Providence, RI 02906, USAMolecular 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.https://www.mdpi.com/1999-4915/15/3/737molecular HIV clustersphylogeneticsmolecular epidemiologyHIV transmission networkscontact tracingnear-real-time data integration |
| spellingShingle | Mark Howison Fizza S. Gillani Vlad Novitsky Jon A. Steingrimsson John Fulton Thomas Bertrand Katharine Howe Anna Civitarese Lila Bhattarai Meghan MacAskill Guillermo Ronquillo Joel Hague Casey W. Dunn Utpala Bandy Joseph W. Hogan Rami Kantor An Automated Bioinformatics Pipeline Informing Near-Real-Time Public Health Responses to New HIV Diagnoses in a Statewide HIV Epidemic Viruses molecular HIV clusters phylogenetics molecular epidemiology HIV transmission networks contact tracing near-real-time data integration |
| 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 | molecular HIV clusters phylogenetics molecular epidemiology HIV transmission networks contact tracing near-real-time data integration |
| url | https://www.mdpi.com/1999-4915/15/3/737 |
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