Rasmussen David A, Wilkinson Eduan, Vandormael Alain, Tanser Frank, Pillay Deenan, Stadler Tanja, de Oliveira Tulio
Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA.
Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
Virus Evol. 2018 Dec 11;4(2):vey037. doi: 10.1093/ve/vey037. eCollection 2018 Jul.
Despite increasing access to antiretrovirals, HIV incidence in rural KwaZulu-Natal remains among the highest ever reported in Africa. While many epidemiological factors have been invoked to explain such high incidence, widespread human mobility and viral movement suggest that transmission between communities may be a major source of new infections. High cross-community transmission rates call into question how effective increasing the coverage of antiretroviral therapy locally will be at preventing new infections, especially if many new cases arise from external introductions. To help address this question, we use a phylodynamic model to reconstruct epidemic dynamics and estimate the relative contribution of local transmission versus external introductions to overall incidence in KwaZulu-Natal from HIV-1 phylogenies. By comparing our results with population-based surveillance data, we show that we can reliably estimate incidence from viral phylogenies once viral movement in and out of the local population is accounted for. Our analysis reveals that early epidemic dynamics were largely driven by external introductions. More recently, we estimate that 35 per cent (95% confidence interval: 20-60%) of new infections arise from external introductions. These results highlight the growing need to consider larger-scale regional transmission dynamics when designing and testing prevention strategies.
尽管获得抗逆转录病毒药物的机会有所增加,但夸祖鲁-纳塔尔农村地区的艾滋病毒发病率仍然是非洲有记录以来最高的之一。虽然人们援引了许多流行病学因素来解释如此高的发病率,但广泛的人口流动和病毒传播表明,社区间传播可能是新感染的主要来源。高社区间传播率让人质疑在当地增加抗逆转录病毒疗法的覆盖范围对预防新感染的效果如何,特别是如果许多新病例是由外部传入引起的。为了帮助回答这个问题,我们使用系统动力学模型来重建流行动态,并根据HIV-1系统发育来估计本地传播与外部传入对夸祖鲁-纳塔尔总体发病率的相对贡献。通过将我们的结果与基于人群的监测数据进行比较,我们表明,一旦考虑到病毒在当地人群中的进出情况,我们就能从病毒系统发育中可靠地估计发病率。我们的分析表明,早期的流行动态主要由外部传入驱动。最近,我们估计35%(95%置信区间:20%-60%)的新感染是由外部传入引起的。这些结果凸显了在设计和测试预防策略时,越来越需要考虑更大规模的区域传播动态。