Learning transmission dynamics modelling of COVID-19 using comomodels

Solveig A van der Vegt; Liangti Dai; Ioana Bouros; Hui Jia Farm; Richard Creswell; Oscar Dimdore-Miles; Idil Cazimoglu; Sumali Bajaj; Lyle Hopkins; David Seiferth; Fergus Cooper; Chon Lok Lei; David Gavaghan; Ben Lambert

doi:10.1016/j.mbs.2022.108824

Learning transmission dynamics modelling of COVID-19 using comomodels

Math Biosci. 2022 Jul:349:108824. doi: 10.1016/j.mbs.2022.108824. Epub 2022 May 7.

Authors

Affiliations

¹ Doctoral Training Centre, University of Oxford, Oxford, UK; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
² 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.
³ Doctoral Training Centre, University of Oxford, Oxford, UK; Department of Computer Science, University of Oxford, Oxford, UK.
⁴ Department of Computer Science, University of Oxford, Oxford, UK.
⁵ Atmospheric, Oceanic and Planetary Physics Department, University of Oxford, Oxford, UK.
⁶ Doctoral Training Centre, University of Oxford, Oxford, UK.
⁷ 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.
⁸ 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: [email protected].

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.

Keywords: COVID-19; Compartmental models; Epidemiology; Infectious disease modelling; Pedagogy; Population dynamics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19*
Epidemics*
Humans
Learning
Models, Theoretical