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Fan Q, Zhang J, Pan X, Ding X, Xing H, Feng Y, Li X, Zhong P, Zhao H, Cheng W, Jiang J, Chen W, Zhou X, Guo Z, Xia Y, Chai C, Jiang J. Insights into the molecular network characteristics of major HIV-1 subtypes in developed Eastern China: a study based on comprehensive molecular surveillance data. Infection 2024:10.1007/s15010-024-02389-5. [PMID: 39325352 DOI: 10.1007/s15010-024-02389-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 08/31/2024] [Indexed: 09/27/2024]
Abstract
PURPOSE This study aimed to conduct a comprehensive molecular epidemiology study of major HIV-1 subtypes in developed Eastern China (Zhejiang Province). METHODS Plasma samples and epidemiological information were collected from 4180 newly diagnosed HIV-1 positive patients in Zhejiang Province in 2021. Pol sequences were obtained to determine the subtypes via multiple analytical tools. HIV-1 molecular networks were constructed on the basis of genetic distances to analyze transmission patterns among major subtypes. Furthermore, the birth-death skyline (BDSKY) model was utilized to estimate the transmission risks associated with large clusters (LCs). RESULTS In 4180 patients, 3699 (88.49%) pol sequences were successfully obtained and classified into four subtype groups. In the networks under an optimal genetic distance of 0.01 substitutions/site, the majority of links (74.52%, 1383/1856) involved individuals within the same city, highlighting the predominant role of local transmission in driving the HIV-1 epidemic. In the CRF07_BC, CRF01_AE, and others/URFs networks, men who have sex with men (MSM) were the primary sexual transmission population, with the younger MSM group (< 30 years old) exhibiting higher linkage frequencies. Within the CRF08_BC network, 93.98% of individuals were infected primarily through heterosexual contact and had a significantly greater risk of localized clustering than other subtypes did. Moreover, fifteen identified LCs were predominantly transmitted through commercial heterosexual contact (CHC), exhibiting localized clustering and high potential for sustained diffusion. CONCLUSIONS Overall, our findings reveal a diverse and heterogeneous distribution of HIV-1 subtypes in Zhejiang Province, with noticeable variations in hotspots across different geographic areas and populations.
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Affiliation(s)
- Qin Fan
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
- AIDS Testing Professional Committee, Zhejiang Provincial Association of AIDS and STDs Control and Prevention, Hangzhou, Zhejiang, 310051, P.R. China
| | - Jiafeng Zhang
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
- AIDS Testing Professional Committee, Zhejiang Provincial Association of AIDS and STDs Control and Prevention, Hangzhou, Zhejiang, 310051, P.R. China
| | - Xiaohong Pan
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Xiaobei Ding
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
- AIDS Testing Professional Committee, Zhejiang Provincial Association of AIDS and STDs Control and Prevention, Hangzhou, Zhejiang, 310051, P.R. China
| | - Hui Xing
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, P.R. China
| | - Yi Feng
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, P.R. China
| | - Xingguang Li
- Guoke Ningbo Life Science and Health Industry Research Institute, Ningbo, 315000, P.R. China
| | - Ping Zhong
- Department of AIDS and STD, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, 200336, P.R. China
| | - Hehe Zhao
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, P.R. China
| | - Wei Cheng
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Jun Jiang
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Wanjun Chen
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Xin Zhou
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Zhihong Guo
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
| | - Yan Xia
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China
- AIDS Testing Professional Committee, Zhejiang Provincial Association of AIDS and STDs Control and Prevention, Hangzhou, Zhejiang, 310051, P.R. China
| | - Chengliang Chai
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China.
| | - Jianmin Jiang
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, No.3399, Binsheng Road, Hangzhou, Zhejiang, 310051, People's Republic of China.
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Weaver S, Dávila Conn VM, Ji D, Verdonk H, Ávila-Ríos S, Leigh Brown AJ, Wertheim JO, Kosakovsky Pond SL. AUTO-TUNE: selecting the distance threshold for inferring HIV transmission clusters. FRONTIERS IN BIOINFORMATICS 2024; 4:1400003. [PMID: 39086842 PMCID: PMC11289888 DOI: 10.3389/fbinf.2024.1400003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/17/2024] [Indexed: 08/02/2024] Open
Abstract
Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained heterosexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.
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Affiliation(s)
- Steven Weaver
- Center for Viral Evolution, Temple University, Philadelphia, PA, United States
| | - Vanessa M. Dávila Conn
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Daniel Ji
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Hannah Verdonk
- Center for Viral Evolution, Temple University, Philadelphia, PA, United States
| | | | - Andrew J. Leigh Brown
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Joel O. Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
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3
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Sun C, Fang R, Salemi M, Prosperi M, Rife Magalis B. DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction. PLoS Comput Biol 2024; 20:e1011351. [PMID: 38598563 PMCID: PMC11034642 DOI: 10.1371/journal.pcbi.1011351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 04/22/2024] [Accepted: 03/11/2024] [Indexed: 04/12/2024] Open
Abstract
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.
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Affiliation(s)
- Chaoyue Sun
- Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States of America
- Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, Florida, United States of America
| | - Marco Salemi
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Mattia Prosperi
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
- Department of Epidemiology, University of Florida, Gainesville, Florida, United States of America
| | - Brittany Rife Magalis
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
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4
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Weaver S, Dávila-Conn V, Ji D, Verdonk H, Ávila-Ríos S, Leigh Brown AJ, Wertheim JO, Kosakovsky Pond SL. AUTO-TUNE: SELECTING THE DISTANCE THRESHOLD FOR INFERRING HIV TRANSMISSION CLUSTERS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.11.584522. [PMID: 38559140 PMCID: PMC10979987 DOI: 10.1101/2024.03.11.584522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Molecular surveillance of viral pathogens and inference of transmission networks from genomic data play an increasingly important role in public health efforts, especially for HIV-1. For many methods, the genetic distance threshold used to connect sequences in the transmission network is a key parameter informing the properties of inferred networks. Using a distance threshold that is too high can result in a network with many spurious links, making it difficult to interpret. Conversely, a distance threshold that is too low can result in a network with too few links, which may not capture key insights into clusters of public health concern. Published research using the HIV-TRACE software package frequently uses the default threshold of 0.015 substitutions/site for HIV pol gene sequences, but in many cases, investigators heuristically select other threshold parameters to better capture the underlying dynamics of the epidemic they are studying. Here, we present a general heuristic scoring approach for tuning a distance threshold adaptively, which seeks to prevent the formation of giant clusters. We prioritize the ratio of the sizes of the largest and the second largest cluster, maximizing the number of clusters present in the network. We apply our scoring heuristic to outbreaks with different characteristics, such as regional or temporal variability, and demonstrate the utility of using the scoring mechanism's suggested distance threshold to identify clusters exhibiting risk factors that would have otherwise been more difficult to identify. For example, while we found that a 0.015 substitutions/site distance threshold is typical for US-like epidemics, recent outbreaks like the CRF07_BC subtype among men who have sex with men (MSM) in China have been found to have a lower optimal threshold of 0.005 to better capture the transition from injected drug use (IDU) to MSM as the primary risk factor. Alternatively, in communities surrounding Lake Victoria in Uganda, where there has been sustained hetero-sexual transmission for many years, we found that a larger distance threshold is necessary to capture a more risk factor-diverse population with sparse sampling over a longer period of time. Such identification may allow for more informed intervention action by respective public health officials.
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Affiliation(s)
- Steven Weaver
- Center for Viral Evolution, Temple University, Philadelphia, PA, USA
| | - Vanessa Dávila-Conn
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Daniel Ji
- Department of Computer Science & Engineering, UC San Diego, La Jolla, CA 92093, USA
| | - Hannah Verdonk
- Center for Viral Evolution, Temple University, Philadelphia, PA, USA
| | - Santiago Ávila-Ríos
- Center for Research in Infectious Diseases, National Institute of Respiratory Diseases, Mexico City, Mexico
| | - Andrew J Leigh Brown
- School of Biological Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Joel O Wertheim
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
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5
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Zhou Y, Lu J, Zhang Z, Sun Q, Xu X, Hu H. Characteristics of the different HIV-1 risk populations based on the genetic transmission network of the newly diagnosed HIV cases in Jiangsu, Eastern China. Heliyon 2023; 9:e22927. [PMID: 38125421 PMCID: PMC10730745 DOI: 10.1016/j.heliyon.2023.e22927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/18/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction The HIV-1 prevalence has been steadily increasing in Jiangsu, China. HIV-1 genetic transmission network can be used to explore the transmission kinetics and precision intervention in high-risk populations. Thus, we generated an HIV-1 genetic transmission network, explored key risk populations based on different risk factors and found out the risk factors for HIV-1 prevention and control among the newly-diagnosed HIV-1 cases from 2017 to 2018. Method We amplified the HIV-1 pol sequences from the plasma samples of the newly-diagnosed HIV-1 cases from 2017 to 2018 and obtained the infection data from The National HIV/AIDS Surveillance System. HIV-Trace and Cytoscape Software were both used to construct the HIV-1 genetic network with a gene distance of <0.005. The R software was used to analyze the risk factors for inclusion into the network. Results We obtained 3362 sequences with the pol gene region, of which 3316 contained detailed individual information. CRF01_AE accounted for 42.3 % of the HIV-1 subtypes in the samples. The median CD4+T lymphocyte count was 329 cells/μL in 2017 and 313 cells/μL in 2018. At the gene distance threshold of 0.005, 481 sequences were incorporated into the HIV-1 gene network, constructing 202 clusters. Age over 60 years old, heterosexual transmission route, subtype (CRF105_0107, CRF55_01 B, and CRF67_01 B) and CD4+T lymphocyte count (>200) were the risk factors influencing inclusion into the HIV-1 gene network. Moreover, south Jiangsu cities had higher inclusion in the network. Thus, key risk populations in the clusters with different transmission routes, new emerging subtypes, drug resistance nodes, and individuals above 60 years of age in the network represented the critical risk populations that should be focused more on for intervention. Conclusion The HIV-1 genetic transmission network is adept at discovering undiagnosed HIV-infected cases and linking all diagnosed cases for determination of risk infections. Therefore we should pay more attention to these risk infections with further investigation and intervention, helping to achieve the goal of 95 % use combination prevention from the World Health Organization, and push to end AIDS epidemic.
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Affiliation(s)
- Ying Zhou
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Jing Lu
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Zhi Zhang
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Qi Sun
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Xiaoqin Xu
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
| | - Haiyang Hu
- Institute of AIDS/STD Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, 210009, China
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6
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DeGruttola V, Nakazawa M, Lin T, Liu J, Goyal R, Little S, Tu X, Mehta S. Modeling homophily in dynamic networks with application to HIV molecular surveillance. BMC Infect Dis 2023; 23:656. [PMID: 37794364 PMCID: PMC10548762 DOI: 10.1186/s12879-023-08598-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/11/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Efforts to control the HIV epidemic can benefit from knowledge of the relationships between the characteristics of people who have transmitted HIV and those who became infected by them. Investigation of this relationship is facilitated by the use of HIV genetic linkage analyses, which allows inference about possible transmission events among people with HIV infection. Two persons with HIV (PWH) are considered linked if the genetic distance between their HIV sequences is less than a given threshold, which implies proximity in a transmission network. The tendency of pairs of nodes (in our case PWH) that share (or differ in) certain attributes to be linked is denoted homophily. Below, we describe a novel approach to modeling homophily with application to analyses of HIV viral genetic sequences from clinical series of participants followed in San Diego. Over the 22-year period of follow-up, increases in cluster size results from HIV transmissions to new people from those already in the cluster-either directly or through intermediaries. METHODS Our analytical approach makes use of a logistic model to describe homophily with regard to demographic, clinical, and behavioral characteristics-that is we investigate whether similarities (or differences) between PWH in these characteristics are associated with their sequences being linked. To investigate the performance of our methods, we conducted on a simulation study for which data sets were generated in a way that reproduced the structure of the observed database. RESULTS Our results demonstrated strong positive homophily associated with hispanic ethnicity, and strong negative homophily, with birth year difference. The second result implies that the larger the difference between the age of a newly-infected PWH and the average age for an available cluster, the lower the odds of a newly infected person joining that cluster. We did not observe homophily associated with prior diagnosis of sexually transmitted diseases. Our simulation studies demonstrated the validity of our approach for modeling homophily, by showing that the estimates it produced matched the specified values of the statistical network generating model. CONCLUSIONS Our novel methods provide a simple and flexible statistical network-based approach for modeling the growth of viral (or other microbial) genetic clusters from linkage to new infections based on genetic distance.
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Affiliation(s)
- Victor DeGruttola
- Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity Science, University of California, 9500 Gilman Dr., 92093-0628, San Diego, La Jolla, CA, USA.
| | | | - Tuo Lin
- Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity Science, University of California, 9500 Gilman Dr., 92093-0628, San Diego, La Jolla, CA, USA
| | - Jinyuan Liu
- Vanderbilt University, Department of Medicine, Nashville, USA
| | - Ravi Goyal
- Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, USA
| | - Susan Little
- Division of Infectious Diseases and Global Public Health, University of California San Diego, La Jolla, CA, USA
| | - Xin Tu
- Division of Biostatistics and Bioinformatics Herbert Wertheim School of Public Health and Human Longevity Science, University of California, 9500 Gilman Dr., 92093-0628, San Diego, La Jolla, CA, USA
| | - Sanjay Mehta
- Veterans Affairs, San Diego Healthcare System, San Diego, CA, USA
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7
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Cholette F, Lazarus L, Macharia P, Thompson LH, Githaiga S, Mathenge J, Walimbwa J, Kuria I, Okoth S, Wambua S, Albert H, Mwangi P, Adhiambo J, Kasiba R, Juma E, Battacharjee P, Kimani J, Sandstrom P, Meyers AFA, Joy JB, Thomann M, McLaren PJ, Shaw S, Mishra S, Becker ML, McKinnon L, Lorway R. Community Insights in Phylogenetic HIV Research: The CIPHR Project Protocol. Glob Public Health 2023; 18:2269435. [PMID: 37851872 DOI: 10.1080/17441692.2023.2269435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 10/04/2023] [Indexed: 10/20/2023]
Abstract
Inferring HIV transmission networks from HIV sequences is gaining popularity in the field of HIV molecular epidemiology. However, HIV sequences are often analyzed at distance from those affected by HIV epidemics, namely without the involvement of communities most affected by HIV. These remote analyses often mean that knowledge is generated in absence of lived experiences and socio-economic realities that could inform the ethical application of network-derived information in 'real world' programmes. Procedures to engage communities are noticeably absent from the HIV molecular epidemiology literature. Here we present our team's protocol for engaging community activists living in Nairobi, Kenya in a knowledge exchange process - The CIPHR Project (Community Insights in Phylogenetic HIV Research). Drawing upon a community-based participatory approach, our team will (1) explore the possibilities and limitations of HIV molecular epidemiology for key population programmes, (2) pilot a community-based HIV molecular study, and (3) co-develop policy guidelines on conducting ethically safe HIV molecular epidemiology. Critical dialogue with activist communities will offer insight into the potential uses and abuses of using such information to sharpen HIV prevention programmes. The outcome of this process holds importance to the development of policy frameworks that will guide the next generation of the global response.
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Affiliation(s)
- François Cholette
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Lisa Lazarus
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Pascal Macharia
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - Laura H Thompson
- Sexually Transmitted and Blood-Borne Infections Surveillance Division, Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Ottawa, Canada
| | - Samuel Githaiga
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - John Mathenge
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | | | - Irene Kuria
- Key Population Consortium of Kenya, Nairobi, Kenya
| | - Silvia Okoth
- Bar Hostess Empowerment and Support Programme, Nairobi, Kenya
| | | | - Harrison Albert
- Health Options for Young Men on HIV/AIDS and STIs (HOYMAS), Nairobi, Kenya
| | - Peninah Mwangi
- Bar Hostess Empowerment and Support Programme, Nairobi, Kenya
| | - Joyce Adhiambo
- Partners for Health Development in Africa (PHDA), Nairobi, Kenya
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
| | | | - Esther Juma
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
| | | | - Joshua Kimani
- Sex Worker Outreach Programme (SWOP), Nairobi, Kenya
- Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya
| | - Paul Sandstrom
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Adrienne F A Meyers
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Jeffrey B Joy
- British Columbia Centre for Excellence in HIV/AIDS (BCCfE), St. Paul's Hospital, Vancouver, Canada
- Division of Infectious Diseases, Department of Medicine, University of British Columbia, Vancouver, Canada
- Bioinformatics Programme, University of British Columbia, Vancouver, Canada
| | - Matthew Thomann
- Department of Anthropology, University of Maryland, College Park, MD, USA
| | - Paul J McLaren
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Sexually Transmitted and Blood-Borne Infections, National Microbiology Laboratory at JC Wilt Infectious Diseases Research Centre, Public Health Agency of Canada, Winnipeg, Canada
| | - Souradet Shaw
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Sharmistha Mishra
- MAP Centre for Urban Health Solutions, St. Michael's Hospital, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada
| | - Marissa L Becker
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
| | - Lyle McKinnon
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Canada
- Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya
- Centre for the AIDS Programme of Research in South Africa, Durban, South Africa
| | - Robert Lorway
- Institute for Global Public Health, Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada
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Fan Q, Zhang J, Luo M, Feng Y, Ge R, Yan Y, Zhong P, Ding X, Xia Y, Guo Z, Pan X, Chai C. Molecular Genetics and Epidemiological Characteristics of HIV-1 Epidemic Strains in Various Sexual Risk Behaviour Groups in Developed Eastern China, 2017-2020. Emerg Microbes Infect 2022; 11:2326-2339. [PMID: 36032035 PMCID: PMC9542350 DOI: 10.1080/22221751.2022.2119167] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Qin Fan
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Jiafeng Zhang
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Mingyu Luo
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Yi Feng
- State Key Laboratory for Infectious Disease Prevention and Control, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, People’s Republic of China
| | - Rui Ge
- Division of AIDS/TB Prevention and Control, Jiaxing Municipal Center for Disease Control and Prevention, Jiaxing 314050, People’s Republic of China
| | - Yong Yan
- Division of AIDS/TB Prevention and Control, Jiaxing Municipal Center for Disease Control and Prevention, Jiaxing 314050, People’s Republic of China
| | - Ping Zhong
- Department of AIDS and STD, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200051, People’s Republic of China
| | - Xiaobei Ding
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Yan Xia
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Zhihong Guo
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Xiaohong Pan
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
| | - Chengliang Chai
- Department of HIV/AIDS and STDs Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, People’s Republic of China
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9
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Molldrem S, Smith AKJ, McClelland A. Predictive analytics in HIV surveillance require new approaches to data ethics, rights, and regulation in public health. CRITICAL PUBLIC HEALTH 2022. [DOI: 10.1080/09581596.2022.2113035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Stephen Molldrem
- Bioethics and Health Humanities, The University of Texas Medical Branch at Galveston, Texas, United States
| | - Anthony K J Smith
- Centre for Social Research in Health, UNSW Sydney, New South Wales, Australia
| | - Alexander McClelland
- Institute of Criminology and Criminal Justice, Carleton University, Ottawa, Ontario, Canada
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