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Learning transmission dynamics modelling of COVID-19 using comomodels.
- Source :
-
Mathematical Biosciences . Jul2022, Vol. 349, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
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. • Compartmental models of transmission dynamics have been important determinants of public health policy for COVID-19. • Many important characteristics of the spread of COVID-19 can be deduced from relatively simple models. • The comomodels package and its associated GUI allow users to learn the characteristics of complex compartmental models in incremental fashion. • Estimating model parameters from data is generally difficult, and sensitivity analyses are key. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00255564
- Volume :
- 349
- Database :
- Academic Search Index
- Journal :
- Mathematical Biosciences
- Publication Type :
- Periodical
- Accession number :
- 157389534
- Full Text :
- https://doi.org/10.1016/j.mbs.2022.108824