Compositional modelling of immune response and virus transmission dynamics.

COVID-19 epidemics immune response multi-scale modelling process calculi

Journal

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
ISSN: 1471-2962
Titre abrégé: Philos Trans A Math Phys Eng Sci
Pays: England
ID NLM: 101133385

Informations de publication

Date de publication:
03 Oct 2022
Historique:
entrez: 15 8 2022
pubmed: 16 8 2022
medline: 17 8 2022
Statut: ppublish

Résumé

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'.

Identifiants

pubmed: 35965463
doi: 10.1098/rsta.2021.0307
pmc: PMC9376723
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

20210307

Subventions

Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V027956/1
Pays : United Kingdom

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Auteurs

W Waites (W)

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.

M Cavaliere (M)

Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.

V Danos (V)

Département d'Informatique, École Normale Supérieure, Paris, France.

R Datta (R)

Datta Enterprises LLC, San Francisco, CA, USA.

R M Eggo (RM)

Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.

T B Hallett (TB)

MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.

D Manheim (D)

Technion, Israel Institute of Technology, Haifa, Israel.

J Panovska-Griffiths (J)

The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
The Queen's College, University of Oxford, Oxford, UK.

T W Russell (TW)

Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.

V I Zarnitsyna (VI)

Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA, USA.

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