Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA, United States of America.
Yale University School of Medicine, New Haven, CT, United States of America.
PLoS One. 2022 Aug 25;17(8):e0266216. doi: 10.1371/journal.pone.0266216. eCollection 2022.
Integration of genetic, social network, and spatial data has the potential to improve understanding of transmission dynamics in established HCV epidemics. Sequence data were analyzed from 63 viremic people who inject drugs recruited in the Boston area through chain referral or time-location sampling. HCV subtype 1a was most prevalent (57.1%), followed by subtype 3a (33.9%). The phylogenetic distances between sequences were no shorter comparing individuals within versus across networks, nor by location or time of first injection. Social and spatial networks, while interesting, may be too ephemeral to inform transmission dynamics when the date and location of infection are indeterminate.
遗传、社交网络和空间数据的整合有可能增进对已确立 HCV 流行地区传播动态的了解。通过连锁式推荐或定时定位抽样法,从波士顿地区招募的 63 名静脉注射吸毒者的病毒血症患者中分析了序列数据。最常见的 HCV 亚型为 1a(57.1%),其次为 3a(33.9%)。比较网络内个体与网络间个体、按地理位置或首次注射时间,序列间的系统发育距离均无显著差异。尽管社交和空间网络很有趣,但当感染日期和地点不确定时,它们可能过于短暂,无法为传播动态提供信息。