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Journal article

Compositional modelling of immune response and virus transmission dynamics

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’.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rsta.2021.0307

Authors

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Role:
Author
ORCID:
0000-0002-7759-6805
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Role:
Author
ORCID:
0000-0002-0362-6717


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Funder identifier:
10.13039/100010269
Grant:
206250/Z/17/Z
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Funder identifier:
https://ror.org/03x94j517
Grant:
MR/V027956/1


Publisher:
The Royal Society
Journal:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences More from this journal
Volume:
380
Issue:
2233
Pages:
20210307
Article number:
20210307
Publication date:
2022-08-15
Acceptance date:
2022-03-16
DOI:
EISSN:
1471-2962
ISSN:
1364503X, 1364-503X


Language:
English
Keywords:
Pubs id:
1276198
Local pid:
pubs:1276198
Source identifiers:
3805706
Deposit date:
2026-02-27
ARK identifier:
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