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Learning transmission dynamics modelling of COVID-19 using comomodels
van der Vegt, Solveig A.1,2; Dai, Liangti1,3; Bouros, Ioana1,4; Farm, Hui Jia4; Creswell, Richard4; Dimdore-Miles, Oscar7; Cazimoglu, Idil1; Bajaj, Sumali1; Hopkins, Lyle1,4; Seiferth, David1; Cooper, Fergus1,4; Lei, Chon Lok5,6; Gavaghan, David1,4; Lambert, Ben1,8
2022-05-07
Source PublicationMathematical Biosciences
ISSN0025-5564
Volume349Pages:108824
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.

KeywordCompartmental Models Covid-19 Epidemiology Infectious Disease Modelling Pedagogy Population Dynamics
DOI10.1016/j.mbs.2022.108824
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology
WOS SubjectBiology ; Mathematical & Computational Biology
WOS IDWOS:000808468100001
PublisherELSEVIER SCIENCE INC, STE 800, 230 PARK AVE, NEW YORK, NY 10169
Scopus ID2-s2.0-85131134399
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionInstitute of Translational Medicine
DEPARTMENT OF BIOMEDICAL SCIENCES
Co-First Authorvan der Vegt, Solveig A.; Dai, Liangti; Bouros, Ioana; Farm, Hui Jia; Creswell, Richard; Dimdore-Miles, Oscar
Corresponding AuthorLambert, Ben
Affiliation1.Doctoral Training Centre, University of Oxford, Oxford, United Kingdom
2.Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
3.MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
4.Department of Computer Science, University of Oxford, Oxford, United Kingdom
5.Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, China
6.Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, China
7.Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, United Kingdom
8.Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom
Recommended Citation
GB/T 7714
van der Vegt, Solveig A.,Dai, Liangti,Bouros, Ioana,et al. Learning transmission dynamics modelling of COVID-19 using comomodels[J]. Mathematical Biosciences, 2022, 349, 108824.
APA 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 (2022). Learning transmission dynamics modelling of COVID-19 using comomodels. Mathematical Biosciences, 349, 108824.
MLA van der Vegt, Solveig A.,et al."Learning transmission dynamics modelling of COVID-19 using comomodels".Mathematical Biosciences 349(2022):108824.
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