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Learning transmission dynamics modelling of COVID-19 using comomodels
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 subst...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077823/ https://www.ncbi.nlm.nih.gov/pubmed/35537550 http://dx.doi.org/10.1016/j.mbs.2022.108824 |
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author | 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 |
author_facet | 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 |
author_sort | van der Vegt, Solveig A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9077823 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Authors. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90778232022-05-09 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 Math Biosci Original Research Article 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. The Authors. Published by Elsevier Inc. 2022-07 2022-05-07 /pmc/articles/PMC9077823/ /pubmed/35537550 http://dx.doi.org/10.1016/j.mbs.2022.108824 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Research Article 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 Learning transmission dynamics modelling of COVID-19 using comomodels |
title | Learning transmission dynamics modelling of COVID-19 using comomodels |
title_full | Learning transmission dynamics modelling of COVID-19 using comomodels |
title_fullStr | Learning transmission dynamics modelling of COVID-19 using comomodels |
title_full_unstemmed | Learning transmission dynamics modelling of COVID-19 using comomodels |
title_short | Learning transmission dynamics modelling of COVID-19 using comomodels |
title_sort | learning transmission dynamics modelling of covid-19 using comomodels |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9077823/ https://www.ncbi.nlm.nih.gov/pubmed/35537550 http://dx.doi.org/10.1016/j.mbs.2022.108824 |
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