Ann M Dennis1, Stéphane Hué2, Rachael Billock3, Sara Levintow3, Joseph Sebastian4, William C Miller5, Joseph J Eron1,3. 1. Division of Infectious Diseases, University of North Carolina at Chapel Hill. 2. London School of Hygiene and Tropical Medicine, United Kingdom. 3. Department of Epidemiology, University of North Carolina at Chapel Hill. 4. Campbell University School of Osteopathic Medicine, South Lillington, North Carolina. 5. Department of Epidemiology, Ohio State University, Columbus.
Abstract
BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) phylodynamics can be used to monitor epidemic trends and help target prevention through identification and characterization of transmission clusters. METHODS: We analyzed HIV-1 pol sequences sampled in North Carolina from 1997 to 2014. Putative clusters were identified using maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference. Active clusters were defined as clusters including internal nodes from 2009 to 2014. Effective reproductive numbers (Re) were estimated using birth-death models for large clusters that expanded ≥2-fold from 2009 to 2014. RESULTS: Of 14 921 persons, 7508 (50%) sequences were identified in 2264 clusters. Only 288 (13%) clusters were active from 2009 to 2014; 37 were large (10-36 members). Compared to smaller clusters, large clusters were increasingly populated by men and younger persons; however, nearly 60% included ≥1 women. Clusters with ≥3 members demonstrated assortative mixing by sex, age, and sample region. Of 15 large clusters with ≥2-fold expansion, nearly all had Re approximately 1 by 2014. CONCLUSIONS: Phylodynamics revealed transmission cluster expansion in this densely sampled region and allowed estimates of Re to monitor active clusters, showing the propensity for steady, onward propagation. Associations with clustering and cluster characteristics vary by cluster size. Harnessing sequence-derived epidemiologic parameters within routine surveillance could allow refined monitoring of local subepidemics.
BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) phylodynamics can be used to monitor epidemic trends and help target prevention through identification and characterization of transmission clusters. METHODS: We analyzed HIV-1 pol sequences sampled in North Carolina from 1997 to 2014. Putative clusters were identified using maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference. Active clusters were defined as clusters including internal nodes from 2009 to 2014. Effective reproductive numbers (Re) were estimated using birth-death models for large clusters that expanded ≥2-fold from 2009 to 2014. RESULTS: Of 14 921 persons, 7508 (50%) sequences were identified in 2264 clusters. Only 288 (13%) clusters were active from 2009 to 2014; 37 were large (10-36 members). Compared to smaller clusters, large clusters were increasingly populated by men and younger persons; however, nearly 60% included ≥1 women. Clusters with ≥3 members demonstrated assortative mixing by sex, age, and sample region. Of 15 large clusters with ≥2-fold expansion, nearly all had Re approximately 1 by 2014. CONCLUSIONS: Phylodynamics revealed transmission cluster expansion in this densely sampled region and allowed estimates of Re to monitor active clusters, showing the propensity for steady, onward propagation. Associations with clustering and cluster characteristics vary by cluster size. Harnessing sequence-derived epidemiologic parameters within routine surveillance could allow refined monitoring of local subepidemics.
Authors: Helena Skar; Maria Axelsson; Ingela Berggren; Anders Thalme; Katarina Gyllensten; Kirsi Liitsola; Henrikki Brummer-Korvenkontio; Pia Kivelä; Erika Spångberg; Thomas Leitner; Jan Albert Journal: J Virol Date: 2010-10-20 Impact factor: 5.103
Authors: Tanja Stadler; Roger Kouyos; Viktor von Wyl; Sabine Yerly; Jürg Böni; Philippe Bürgisser; Thomas Klimkait; Beda Joos; Philip Rieder; Dong Xie; Huldrych F Günthard; Alexei J Drummond; Sebastian Bonhoeffer Journal: Mol Biol Evol Date: 2011-09-02 Impact factor: 16.240
Authors: Alexandra M Oster; Joel O Wertheim; Angela L Hernandez; Marie Cheryl Bañez Ocfemia; Neeraja Saduvala; H Irene Hall Journal: J Acquir Immune Defic Syndr Date: 2015-12-01 Impact factor: 3.731
Authors: Erik M Volz; Edward Ionides; Ethan O Romero-Severson; Mary-Grace Brandt; Eve Mokotoff; James S Koopman Journal: PLoS Med Date: 2013-12-10 Impact factor: 11.069
Authors: Marcos Pérez-Losada; Amanda D Castel; Brittany Lewis; Michael Kharfen; Charles P Cartwright; Bruce Huang; Taylor Maxwell; Alan E Greenberg; Keith A Crandall Journal: PLoS One Date: 2017-09-29 Impact factor: 3.240
Authors: Erika Samoff; Victoria Mobley; Michelle Hudgins; Anna Barry Cope; Nicole Dzialowy Adams; Christina R Caputo; Ann M Dennis; Rachael M Billock; Christy A Crowley; Jacquelyn M Clymore; Evelyn Foust Journal: Am J Public Health Date: 2020-01-16 Impact factor: 9.308
Authors: Adiba Hassan; Victor De Gruttola; Yunyin W Hu; Zhijuan Sheng; Kathleen Poortinga; Joel O Wertheim Journal: Clin Infect Dis Date: 2020-12-03 Impact factor: 9.079
Authors: Vlad Novitsky; Jon Steingrimsson; Fizza S Gillani; Mark Howison; Su Aung; Matthew Solomon; Cindy Y Won; Amy Brotherton; Rajeev Shah; Casey Dunn; John Fulton; Thomas Bertrand; Anna Civitarese; Katharine Howe; Theodore Marak; Philip Chan; Utpala Bandy; Nicole Alexander-Scott; Joseph Hogan; Rami Kantor Journal: Open Forum Infect Dis Date: 2021-12-07 Impact factor: 4.423
Authors: Britt Skaathun; Manon Ragonnet-Cronin; Kathleen Poortinga; Zhijuan Sheng; Yunyin W Hu; Joel O Wertheim Journal: Open Forum Infect Dis Date: 2021-04-24 Impact factor: 3.835