Genome-based transmission modeling separates imported tuberculosis from recent transmission within an immigrant population
Genome-based transmission modeling separates imported tuberculosis from recent transmission within an immigrant population
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基于基因组的传播模型将输入性结核病与移民人群中的近期传播区分开来
DOI：
10.1101/226662
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Ayabina D
中科院分区：
文献类型：
--
作者：
Ayabina D
在许多国家，结核病的发病率很低，主要是由来自高负担国家的移民人口造成的。挪威的情况就是如此，该国80%以上的结核病病例是在来自发病率高的国家的移民中发现的。 潜伏期可变、发展率低和结构化的社交网络使区分输入和境内传播成为许多低发病率国家结核病控制工作的一大难题。过去二十年来，挪威发现了属于与非洲之角出生的人相关的异常大的基因型簇的临床结核分支杆菌分离物。我们基于全基因组序列数据对传播进行建模，以估计个体患者的感染时间。通过将这些估计与抵达挪威的时间进行对比，我们逐案估计患者可能是在抵达之前还是之后感染的。独立进口是造成大多数病例的原因，但我们估计约有四分之一的患者在挪威感染了结核病。这项研究阐明了移民社区内的传播动态。我们的方法广泛适用于结核病控制规划可以从了解患者何时何地获得结核病中受益的许多环境。
In many countries the incidence of tuberculosis (TB) is low and is largely shaped by immigrant populations from high-burden countries. This is the case in Norway, where more than 80 % of TB cases are found among immigrants from high-incidence countries. A variable latent period, low rates of evolution and structured social networks make separating import from within-border transmission a major conundrum to TB control efforts in many low-incidence countries. ClinicalMycobacterium tuberculosisisolates belonging to an unusually large genotype cluster associated with people born in the Horn of Africa have been identified in Norway over the last two decades. We modelled transmission based on whole-genome sequence data to estimate infection times for individual patients. By contrasting these estimates with time of arrival in Norway, we estimate on a case-by-case basis whether patients were likely to have been infected before or after arrival. Independent import was responsible for the majority of cases, but we estimate that about one-quarter of the patients had contracted TB in Norway. This study illuminates the transmission dynamics within an immigrant community. Our approach is broadly applicable to many settings where TB control programmes can benefit from understanding when and where patients acquired TB.
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影响因子：
1.7
作者：
Gilmour, M. W.;Graham, M.;Van Domselaar, G.
通讯作者：
Van Domselaar, G.
影响因子：
4.3
作者：
Kenah E;Britton T;Halloran ME;Longini IM Jr
通讯作者：
Longini IM Jr
根据遗传和流行病学数据估计传播：比较传播树的指标
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
M. Kendall;D. Ayabina;C. Colijn
通讯作者：
C. Colijn
影响因子：
4.5
作者：
Guzman Herrador BR;Rønning K;Borgen K;Mannsåker T;Dahle UR
通讯作者：
Dahle UR
DOI：
10.1164/rccm.200702-187oc
发表时间：
2007-11-01
影响因子：
24.7
作者：
Dahle, Ulf R.;Eldholm, Vegard;Heldal, Einar
通讯作者：
Heldal, Einar