e-space
Manchester Metropolitan University's Research Repository

    Compositional modelling of immune response and virus transmission dynamics

    Waites, W, Cavaliere, M ORCID logoORCID: https://orcid.org/0000-0002-4071-6965, Danos, V, Datta, R, Eggo, RM, Hallett, TB, Manheim, D, Panovska-Griffiths, J, Russell, TW and Zarnitsyna, VI (2022) Compositional modelling of immune response and virus transmission dynamics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380 (2233). p. 20210307. ISSN 1364-503X

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (4MB) | Preview

    Abstract

    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’.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    192Downloads
    6 month trend
    63Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record