Compositional modelling of immune response and virus transmission dynamics

• Type of Material: Article
• Authors: William Waites, University of Strathclyde; M. Cavaliere, Manchester Metropolitan University; V. Danos, École Normale Supérieure; R. Datta, Datta Enterprises Llc; R.M. Eggo, University of Strathclyde; T.B. Hallett, Imperial College London; D. Manheim, Technion - Israel Institute of Technology; J. Panovska-Griffiths, Nuffield Department of Medicine; T.W. Russell, University of Strathclyde; Veronika Zarnitsyna, Emory University
• Language: English
• Date: 2022-10-03
• Publisher: Royal Society Publishing
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1098/rsta.2021.0307
• Title of Journal or Parent Work: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
• Volume: 380
• Issue: 2233
• Start Page: 20210307
• End Page: 20210307
• Grant/Funding Information: W.W., D.M. and T.B.H. acknowledge support from the Foundation for Innovative New Diagnostics. W.W. and R.M.E. were supported by MRC grant no. MR/V027956/1. D.M. also acknowledges support from the Center for Effective Altruism’s Long-Term Future Fund. T.W.R. is supported by the Wellcome Trust (206250/Z/17/Z).
• Keywords: immune response; COVID-19; epidemics; Humans; multi-scale modelling; COVID-19 Testing; process calculi; Immunity
• Research Categories: Philosophy; Health Sciences, Immunology

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'.