Learning transmission dynamics modelling of COVID-19 using comomodels
Published in Mathematical Biosciences, 2022
Recommended citation: van der Vegt, S.A., ..., Lei, C.L., Gavaghan, D.J. and Lambert, B. (2022). "Learning transmission dynamics modelling of COVID-19 using comomodels." Mathematical Biosciences, p.108824. https://doi.org/10.1016/j.mbs.2022.108824
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.