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The effect of population structure on the emergence of drug resistance during influenza pandemics

机译:流感大流行期间人口结构对耐药性出现的影响

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摘要

The spread of H5N1 avian influenza and the recent high numbers of confirmed human cases have raised international concern about the possibility of a new pandemic. Therefore, antiviral drugs are now being stockpiled to be used as a first line of defence. The large-scale use of antivirals will however exert a strong selection pressure on the virus, and may lead to the emergence of drug-resistant strains. A few mathematical models have been developed to assess the emergence of drug resistance during influenza pandemics. These models, however, neglected the spatial structure of large populations and the stochasticity of epidemic and demographic processes. To assess the impact of population structure and stochasticity, we modify and extend a previous model of influenza epidemics into a metapopulation model which takes into account the division of large populations into smaller units, and develop deterministic and stochastic versions of the model. We find that the dynamics in a fragmented population is less explosive, and, as a result, prophylaxis will prevent more infections and lead to fewer resistant cases in both the deterministic and stochastic model. While in the deterministic model the final level of resistance during treatment is not affected by fragmentation, in the stochastic model it is. Our results enable us to qualitatively extrapolate the prediction of deterministic, homogeneous-mixing models to more realistic scenarios.
机译:H5N1禽流感的蔓延以及最近确诊的人类病例很多,引起了国际社会对新的大流行可能性的关注。因此,现在正在储备抗病毒药物,以用作第一道防线。但是,大规模使用抗病毒剂将对病毒施加强大的选择压力,并可能导致耐药菌株的出现。已经开发了一些数学模型来评估流感大流行期间耐药性的出现。但是,这些模型忽略了人口众多的空间结构以及流行病和人口统计过程的随机性。为了评估人口结构和随机性的影响,我们修改并扩展了先前的流行性感冒流行病模型,并将其扩展为考虑到将大批人群划分为较小单位的metapopulation模型,并开发了该模型的确定性和随机性版本。我们发现,零散的人群中的动态爆炸性较小,因此,在确定性模型和随机模型中,预防将防止更多的感染并导致更少的耐药病例。虽然在确定性模型中,治疗过程中的最终耐药水平不受片段化的影响,但在随机模型中却受到影响。我们的结果使我们能够将确定性的均质混合模型的预测定性地推算到更现实的场景中。

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