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Learning transmission dynamics modelling of COVID-19 using comomodels.
van der Vegt, Solveig A; Dai, Liangti; Bouros, Ioana; Farm, Hui Jia; Creswell, Richard; Dimdore-Miles, Oscar; Cazimoglu, Idil; Bajaj, Sumali; Hopkins, Lyle; Seiferth, David; Cooper, Fergus; Lei, Chon Lok; Gavaghan, David; Lambert, Ben.
  • van der Vegt SA; Doctoral Training Centre, University of Oxford, Oxford, UK; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
  • Dai L; Doctoral Training Centre, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
  • Bouros I; Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK.
  • Farm HJ; Department of Computer Science, University of Oxford, Oxford, UK.
  • Creswell R; Department of Computer Science, University of Oxford, Oxford, UK.
  • Dimdore-Miles O; Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, UK.
  • Cazimoglu I; Doctoral Training Centre, University of Oxford, Oxford, UK.
  • Bajaj S; Doctoral Training Centre, University of Oxford, Oxford, UK.
  • Hopkins L; Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK.
  • Seiferth D; Doctoral Training Centre, University of Oxford, Oxford, UK.
  • Cooper F; Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK.
  • Lei CL; Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region of China; Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region of China.
  • Gavaghan D; Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK.
  • Lambert B; Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK. Electronic address: ben.c.lambert@gmail.com.
Math Biosci ; 349: 108824, 2022 07.
Article in English | MEDLINE | ID: covidwho-1821409
ABSTRACT
The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignettes.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Limits: Humans Language: English Journal: Math Biosci Year: 2022 Document Type: Article Affiliation country: J.mbs.2022.108824

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 Limits: Humans Language: English Journal: Math Biosci Year: 2022 Document Type: Article Affiliation country: J.mbs.2022.108824