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Rule-based epidemic models.
Waites, W; Cavaliere, M; Manheim, D; Panovska-Griffiths, J; Danos, V.
  • Waites W; School of Informatics, University of Edinburgh, Edinburgh, UK; Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK.
  • Cavaliere M; Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.
  • Manheim D; University of Haifa Health and Risk Communication Research Center, Haifa, Israel.
  • Panovska-Griffiths J; The Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Institute for Global Health, University College London, London, UK; The Queen's College, University of Oxford, Oxford, UK.
  • Danos V; School of Informatics, University of Edinburgh, Edinburgh, UK; Département d'Informatique, École Normale Supérieure, Paris, France.
J Theor Biol ; 530: 110851, 2021 12 07.
Article in English | MEDLINE | ID: covidwho-1768377
ABSTRACT
Rule-based models generalise reaction-based models with reagents that have internal state and may be bound together to form complexes, as in chemistry. An important class of system that would be intractable if expressed as reactions or ordinary differential equations can be efficiently simulated when expressed as rules. In this paper we demonstrate the utility of the rule-based approach for epidemiological modelling presenting a suite of seven models illustrating the spread of infectious disease under different scenarios wearing masks, infection via fomites and prevention by hand-washing, the concept of vector-borne diseases, testing and contact tracing interventions, disease propagation within motif-structured populations with shared environments such as schools, and superspreading events. Rule-based models allow to combine transparent modelling approach with scalability and compositionality and therefore can facilitate the study of aspects of infectious disease propagation in a richer context than would otherwise be feasible.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics Language: English Journal: J Theor Biol Year: 2021 Document Type: Article Affiliation country: J.jtbi.2021.110851

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics Language: English Journal: J Theor Biol Year: 2021 Document Type: Article Affiliation country: J.jtbi.2021.110851