Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
Philos Trans A Math Phys Eng Sci. 2022 Oct 3;380(2233):20210307. doi: 10.1098/rsta.2021.0307. Epub 2022 Aug 15.
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'.
传染病传播模型通常是根据个体或群体之间的动态来制定的,这些过程包括个体的疾病进展或康复等现象,但不涉及潜在的生物学过程。此外,这些模型的构建通常是整体式的:它们不允许人们轻易地修改所涉及的过程或纳入新的过程,也不允许在不同的尺度上组合模型。我们展示了如何构建一个简单的呼吸道病毒免疫反应模型和一个传播模型,使用一组易于修改的规则来允许进一步细化和合并这两个模型。免疫反应模型再现了 COVID-19 的 PCR 检测的预期反应曲线,并暗示了传染性的长尾分布,反映了个体的异质性。当这个免疫反应模型与传播模型结合时,可以再现之前报告的沿着传染病轨迹的病毒载量在人群中的分布的变化。本文是主题为“现实生活中的传染病建模的技术挑战及其克服方法”的一部分。