Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza and Streptococcus pneumoniae.
Time and dose-dependent risk of pneumococcal pneumonia following influenza: a model for within-host interaction between influenza and Streptococcus pneumoniae.
复制标题
流感后肺炎球菌肺炎的时间和剂量依赖性风险：流感和肺炎链球菌之间宿主内相互作用的模型。
DOI：
10.1098/rsif.2013.0233
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Rohani,Pejman
中科院分区：
文献类型：
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作者：
Shrestha,Sourya;Foxman,Betsy;Dawid,Suzanne;Aiello,AllisonE;Davis,BrianM;Berus,Joshua;Rohani,Pejman
季节性死亡，特别是大流行性流感死亡的很大一部分是由继发性细菌感染引起的。在动物模型中，流感病毒使宿主容易受到肺炎链球菌和金黄色葡萄球菌的严重感染。尽管它很重要，但流感和肺炎球菌之间相互作用的机制性质、它对感染时间和顺序的依赖以及临床和流行病学后果仍不清楚。我们探索了一种免疫介导的病毒-细菌相互作用模型，该模型量化了相互作用的时间和强度。利用从动物模型中获得的丰富知识，以及对病原体特异性免疫动力学动力学的定量理解，我们建立了流感病毒和流感病毒之间免疫介导的相互作用的数学模型。肺部的肺炎。我们使用该模型来检验肺炎球菌侵袭的接种量和时间相对于流感感染的致病作用，以及抗病毒药物在预防严重肺炎球菌疾病方面的效果。我们发现，我们的模型能够捕捉到动物实验中观察到的相互作用的关键特征。该模型预测，在流感感染后的4-6天窗口内引入肺炎球菌会导致侵袭性肺炎的接种量明显低于未感染流感的宿主。此外，我们发现，在流感感染后4天后进行抗病毒治疗并不能预防侵袭性肺炎球菌疾病。这项工作为研究流感和肺炎球菌之间的相互作用提供了一个量化框架，并有可能准确量化这些相互作用。这种量化的了解可以构成有效的临床护理、公共卫生政策和大流行防备的基础。
A significant fraction of seasonal and in particular pandemic influenza deaths are attributed to secondary bacterial infections. In animal models, influenza virus predisposes hosts to severe infection with bothStreptococcus pneumoniaeandStaphylococcus aureus. Despite its importance, the mechanistic nature of the interaction between influenza and pneumococci, its dependence on the timing and sequence of infections as well as the clinical and epidemiological consequences remain unclear. We explore an immune-mediated model of the viral–bacterial interaction that quantifies the timing and the intensity of the interaction. Taking advantage of the wealth of knowledge gained from animal models, and the quantitative understanding of the kinetics of pathogen-specific immunological dynamics, we formulate a mathematical model for immune-mediated interaction between influenza virus andS. pneumoniaein the lungs. We use the model to examine the pathogenic effect of inoculum size and timing of pneumococcal invasion relative to influenza infection, as well as the efficacy of antivirals in preventing severe pneumococcal disease. We find that our model is able to capture the key features of the interaction observed in animal experiments. The model predicts that introduction of pneumococcal bacteria during a 4–6 day window following influenza infection results in invasive pneumonia at significantly lower inoculum size than in hosts not infected with influenza. Furthermore, we find that antiviral treatment administered later than 4 days after influenza infection was not able to prevent invasive pneumococcal disease. This work provides a quantitative framework to study interactions between influenza and pneumococci and has the potential to accurately quantify the interactions. Such quantitative understanding can form a basis for effective clinical care, public health policies and pandemic preparedness.