Learning transmission dynamics modelling of COVID-19 using comomodels
dc.contributor.author | van der Vegt, SA | |
dc.contributor.author | Dai, L | |
dc.contributor.author | Bouros, I | |
dc.contributor.author | Farm, HJ | |
dc.contributor.author | Cresswell, R | |
dc.contributor.author | Dimdore-Miles, O | |
dc.contributor.author | Cazimoglu, I | |
dc.contributor.author | Bajaj, S | |
dc.contributor.author | Hopkins, L | |
dc.contributor.author | Seiferth, D | |
dc.contributor.author | Cooper, F | |
dc.contributor.author | Lei, CL | |
dc.contributor.author | Gavaghan, D | |
dc.contributor.author | Lambert, B | |
dc.date.accessioned | 2022-04-12T08:09:55Z | |
dc.date.issued | 2022-05-07 | |
dc.date.updated | 2022-04-11T15:03:05Z | |
dc.description.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 | en_GB |
dc.identifier.citation | Article 108824 | en_GB |
dc.identifier.doi | 10.1016/j.mbs.2022.108824 | |
dc.identifier.uri | http://hdl.handle.net/10871/129347 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2022 Published by Elsevier Inc. Open access under a CC BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.title | Learning transmission dynamics modelling of COVID-19 using comomodels | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2022-04-12T08:09:55Z | |
dc.identifier.issn | 1879-3134 | |
dc.description | This is the author accepted manuscript. The final version is available on open access from Elsevier via the DOI in this record | en_GB |
dc.identifier.journal | Mathematical Biosciences | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-04-11 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2022-04-11 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2022-04-11T15:03:09Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2022-05-11T13:04:30Z | |
refterms.panel | B | en_GB |
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Except where otherwise noted, this item's licence is described as © 2022 Published by Elsevier Inc. Open access under a CC BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/