上海大学学报(自然科学版) ›› 2021, Vol. 27 ›› Issue (6): 1138-1143.doi: 10.12066/j.issn.1007-2861.2195

• 研究论文 • 上一篇    下一篇

北京 MSM 人群 HIV 传播的贝叶斯推断

程金瑾, 金鑫, 汪圳, 娄洁()   

  1. 上海大学 理学院, 上海 200444
  • 收稿日期:2019-12-31 出版日期:2021-12-31 发布日期:2020-02-02
  • 通讯作者: 娄洁 E-mail:Jie.lou@126.com
  • 作者简介:娄 洁(1973—), 女, 教授, 博士生导师, 博士, 研究方向为生物数学. E-mail: Jie.lou@126.com
  • 基金资助:
    国家自然科学基金资助项目(11771277)

Bayesian inference of HIV transmission in MSM population in Beijing

CHENG Jinjin, JIN Xin, WANG Zhen, LOU Jie()   

  1. College of Sciences, Shanghai University, Shanghai 200444, China
  • Received:2019-12-31 Online:2021-12-31 Published:2020-02-02
  • Contact: LOU Jie E-mail:Jie.lou@126.com

摘要:

从北京地区收集了 44 例年轻男男同性恋 (men who have sex with men, MSM) 性行为者的人类免疫缺陷病毒 (human immunodeficiency virus, HIV)-1 基因序列, 利用贝叶斯推断从中提取了一些有价值的流行病学信息, 得到了传播动力学的重要参数: 有效再生数 $R_{\rm e}$. 研究结果显示, 2006 年至 2014 年 HIV/AIDS (acquired immune deficiency syndrome) 的传播速度明显加快, 这可能与过去十多年间中国年轻人群对男男同性恋人群的接受程度越来越高有关. 结果揭示了近十多年来 HIV/AIDS 在北京 MSM 中的传播趋势. 这些信息可以为疫情预测及制定干预措施提供帮助.

关键词: HIV/AIDS, 有效再生数, 贝叶斯推断

Abstract:

In this study, the Bayesian phylogenetic method was used to extract valuable information on epidemiological processes from 44 HIV$^{-1}$ (human immunodeficiency virus$^{-1}$) gene sequences of a young men who have sex with men (MSM) population sampled in Beijing, especially the transmission dynamic parameter, i.e., the effective reproductive number $R_{\rm e}$. These results reveal that the transmission of HIV/acquired immune deficiency syndrome (AIDS) increased rapidly during the period of 2006—2014, which may have a close relation to the growing acceptance of MSM in the past decade among young Chinese. The result reveals the HIV/AIDS transmission trend among the young MSM population over the past decade. Such information is meaningful and can help people understand and predict an epidemic, and establish intervention measures for the future.

Key words: HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome), the effective reproductive number, Bayesian inferences

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