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描述流感病毒和呼吸道合胞病毒之间的相互作用及其对疫情控制的影响。

Characterizing the interactions between influenza and respiratory syncytial viruses and their implications for epidemic control.

机构信息

Max Planck Institute for Infection Biology, Infectious Disease Epidemiology group, Charitéplatz 1, Campus Charité Mitte, 10117, Berlin, Germany.

Hospices Civils de Lyon, Hôpital de la Croix-Rousse, Centre de Biologie Nord, Institut des Agents Infectieux, Laboratoire de Virologie, Lyon, France.

出版信息

Nat Commun. 2024 Nov 20;15(1):10066. doi: 10.1038/s41467-024-53872-4.

DOI:10.1038/s41467-024-53872-4
PMID:39567519
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11579344/
Abstract

Pathogen-pathogen interactions represent a critical but little-understood feature of infectious disease dynamics. In particular, experimental evidence suggests that influenza virus and respiratory syncytial virus (RSV) compete with each other, such that infection with one confers temporary protection against the other. However, such interactions are challenging to study using common epidemiologic methods. Here, we use a mathematical modeling approach, in conjunction with detailed surveillance data from Hong Kong and Canada, to infer the strength and duration of the interaction between influenza and RSV. Based on our estimates, we further utilize our model to evaluate the potential conflicting effects of live attenuated influenza vaccines (LAIV) on RSV burden. We find evidence of a moderate to strong, negative, bidirectional interaction, such that infection with either virus yields 40-100% protection against infection with the other for one to five months. Assuming that LAIV reduces RSV susceptibility in a similar manner, we predict that the impact of such a vaccine at the population level would likely depend greatly on underlying viral circulation patterns. More broadly, we highlight the utility of mathematical models as a tool to characterize pathogen-pathogen interactions.

摘要

病原体-病原体相互作用是传染病动力学中一个关键但尚未被充分理解的特征。特别是,实验证据表明,流感病毒和呼吸道合胞病毒(RSV)相互竞争,以致于感染一种病毒会暂时对另一种病毒产生保护。然而,使用常见的流行病学方法研究这种相互作用具有挑战性。在这里,我们使用数学建模方法,结合来自香港和加拿大的详细监测数据,推断流感和 RSV 之间相互作用的强度和持续时间。基于我们的估计,我们进一步利用我们的模型来评估减毒活流感疫苗(LAIV)对 RSV 负担的潜在冲突影响。我们发现了中度到强的、负的、双向相互作用的证据,即感染其中一种病毒会在一到五个月内对另一种病毒的感染产生 40-100%的保护作用。假设 LAIV 以类似的方式降低 RSV 的易感性,我们预测这种疫苗在人群层面的影响可能在很大程度上取决于潜在的病毒传播模式。更广泛地说,我们强调了数学模型作为一种工具来描述病原体-病原体相互作用的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/e56ec982f773/41467_2024_53872_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/56d62a699ac6/41467_2024_53872_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/faaacf08d44d/41467_2024_53872_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/eb6e035803f6/41467_2024_53872_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/e56ec982f773/41467_2024_53872_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/56d62a699ac6/41467_2024_53872_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/faaacf08d44d/41467_2024_53872_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/eb6e035803f6/41467_2024_53872_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a311/11579344/e56ec982f773/41467_2024_53872_Fig4_HTML.jpg

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