Compositional modelling of immune response and virus transmission dynamics.
Compositional modelling of immune response and virus transmission dynamics.
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免疫反应和病毒传播动力学的组成模型。
DOI：
10.1098/rsta.2021.0307
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发表时间：
2022-10-03
期刊：
影响因子：
5
通讯作者：
Zarnitsyna, V. I.
中科院分区：
文献类型：
--
作者：
Waites, W.;Cavaliere, M.;Danos, V.;Datta, R.;Eggo, R. M.;Hallett, T. B.;Manheim, D.;Panovska-Griffiths, J.;Russell, T. W.;Zarnitsyna, V. I.
传染病的传播模型通常是根据个体或群体之间的动态来制定的，其中每个个体的疾病进展或恢复等过程都是现象学捕获的，而不涉及潜在的生物过程。此外，这些模型的构建通常是整体的：它们不允许人们轻易修改所涉及的过程或包含新的过程，也不允许联合收割机以不同的规模组合模型。我们展示了如何构建一个简单的模型，呼吸道病毒的免疫反应和传播模型，使用一个易于修改的规则集，允许进一步完善和合并两个模型在一起。免疫反应模型再现了COVID-19 PCR检测的预期反应曲线，并暗示了反映个体异质性的传染性长尾分布。这种免疫反应模型，当与传播模型相结合时，再现了先前报道的病毒载量沿着流行病轨迹的人口分布的变化。这篇文章是“模拟现实生活中的流行病的技术挑战和克服这些挑战的例子”主题的一部分。
Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
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影响因子：
9.3
作者：
Hellewell J;Russell TW;SAFER Investigators and Field Study Team;Crick COVID-19 Consortium;CMMID COVID-19 working group;Beale R;Kelly G;Houlihan C;Nastouli E;Kucharski AJ
通讯作者：
Kucharski AJ
DOI：
10.1016/j.entcs.2009.07.070
发表时间：
2009-08-06
影响因子：
--
作者：
Danos, Vincent
通讯作者：
Danos, Vincent
影响因子：
0.5
作者：
Baez, John C.;Master, Jade
通讯作者：
Master, Jade
影响因子：
2.8
作者：
Garira, Winston
通讯作者：
Garira, Winston
DOI：
10.1126/science.abh0635
发表时间：
2021-07-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Hay JA;Kennedy-Shaffer L;Kanjilal S;Lennon NJ;Gabriel SB;Lipsitch M;Mina MJ
通讯作者：
Mina MJ