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Detectable signals of episodic risk effects on acute HIV transmission: Strategies for analyzing transmission systems using genetic data

机译:对急性HIV传播有可检测的突发风险影响信号:使用遗传数据分析传播系统的策略

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Episodic high-risk sexual behavior is common and can have a profound effect on HIV transmission. In a model of HIV transmission among men who have sex with men (MSM), changing the frequency, duration and contact rates of high-risk episodes can take endemic prevalence from zero to 50% and more than double transmissions during acute HIV infection (AHI). Undirected test and treat could be inefficient in the presence of strong episodic risk effects. Partner services approaches that use a variety of control options will be likely to have better effects under these conditions, but the question remains: What data will reveal if a population is experiencing episodic risk effects? HIV sequence data from Montreal reveals genetic clusters whose size distribution stabilizes over time and reflects the size distribution of acute infection outbreaks (AIOs). Surveillance provides complementary behavioral data. In order to use both types of data efficiently, it is essential to examine aspects of models that affect both the episodic risk effects and the shape of transmission trees. As a demonstration, we use a deterministic compartmental model of episodic risk to explore the determinants of the fraction of transmissions during acute HIV infection (AHI) at the endemic equilibrium. We use a corresponding individual-based model to observe AIO size distributions and patterns of transmission within AIO. Episodic risk parameters determining whether AHI transmission trees had longer chains, more clustered transmissions from single individuals, or different mixes of these were explored. Encouragingly for parameter estimation, AIO size distributions reflected the frequency of transmissions from acute infection across divergent parameter sets. Our results show that episodic risk dynamics influence both the size and duration of acute infection outbreaks, thus providing a possible link between genetic cluster size distributions and episodic risk dynamics.
机译:偶发性高风险性行为很普遍,可能对HIV传播产生深远影响。在与男性发生性行为(MSM)的男性之间传播HIV的模型中,改变高危事件的发生频率,持续时间和接触率可使流行率从0%上升到50%,并且在急性HIV感染期间传播超过两倍(AHI) )。在存在强烈的突发性风险影响的情况下,无方向的测试和治疗可能效率不高。在这种情况下,使用各种控制选项的合作伙伴服务方法可能会产生更好的效果,但问题仍然存在:如果某个人群正在经历突发的风险影响,那么哪些数据将显示出来?来自蒙特利尔的HIV序列数据揭示了遗传簇,其大小分布随时间稳定,并反映了急性感染暴发(AIO)的大小分布。监视提供补充的行为数据。为了有效地使用这两种类型的数据,必须检查影响情景风险影响和传播树形状的模型方面。作为演示,我们使用确定性的区间风险模型来探讨在流行性平衡时急性HIV感染(AHI)期间传播比例的决定因素。我们使用相应的基于个人的模型来观察AIO大小分布和AIO中的传输模式。探索了确定AHI传播树是否具有更长链,来自单个个体的更多聚集传播或这些传播的不同混合的突发风险参数。令人鼓舞的是,对于参数估计,AIO大小分布反映了跨不同参数集的急性感染传播的频率。我们的结果表明,突发风险动态影响急性感染暴发的规模和持续时间,从而提供了遗传簇大小分布与突发风险动态之间的可能联系。

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