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Compositional modelling of immune response and virus transmission dynamics.
Waites, W; Cavaliere, M; Danos, V; Datta, R; Eggo, R M; Hallett, T B; Manheim, D; Panovska-Griffiths, J; Russell, T W; Zarnitsyna, V I.
  • Waites W; Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
  • Cavaliere M; Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, UK.
  • Danos V; Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.
  • Datta R; Département d'Informatique, École Normale Supérieure, Paris, France.
  • Eggo RM; Datta Enterprises LLC, San Francisco, CA, USA.
  • Hallett TB; Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
  • Manheim D; MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
  • Panovska-Griffiths J; Technion, Israel Institute of Technology, Haifa, Israel.
  • Russell TW; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Zarnitsyna VI; The Queen's College, University of Oxford, Oxford, UK.
Philos Trans A Math Phys Eng Sci ; 380(2233): 20210307, 2022 Oct 03.
Article in English | MEDLINE | ID: covidwho-1992464
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'.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Experimental Studies / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0307

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Testing / COVID-19 Type of study: Diagnostic study / Experimental Studies / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0307