Doctoral Training Centre, University of Oxford, Oxford, UK; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK.
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.
Math Biosci. 2022 Jul;349:108824. doi: 10.1016/j.mbs.2022.108824. Epub 2022 May 7.
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.
新冠疫情在世界许多地方仍在肆虐。仅在英国,就有一系列数学模型在指导政策制定方面发挥了突出作用。虽然有大量的教学材料可用于理解传染病传播动力学建模的基础知识,但用于理论阐述的相对简单的模型与用于实际建模新冠病毒传播动力学的模型之间存在很大差距。理解这些模型需要相当多的先决知识,并对该领域的新手提出了挑战。在本文中,我们引入了一个开源的 R 包 comomodels,它可以通过一系列微分方程模型来帮助理解建模新冠病毒传播动力学的复杂性。除了基础包之外,我们还描述了一系列学习资源,包括详细的教程和一个交互式的网络界面,允许动态地研究模型属性。然后,我们使用 comomodels 通过 R Markdown 片段说明了在新冠病毒传播过程中的三个关键教训。