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. 2022 Oct 3;380(2233):20220179.
doi: 10.1098/rsta.2022.0179. Epub 2022 Aug 15.

Technical challenges of modelling real-life epidemics and examples of overcoming these

J Panovska-Griffiths  1   2 , W Waites  3 , G J Ackland  4
Affiliations

Technical challenges of modelling real-life epidemics and examples of overcoming these

J Panovska-Griffiths et al. Philos Trans A Math Phys Eng Sci. .

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the importance of mathematical modelling in informing and advising policy decision-making. Effective practice of mathematical modelling has challenges. These can be around the technical modelling framework and how different techniques are combined, the appropriate use of mathematical formalisms or computational languages to accurately capture the intended mechanism or process being studied, in transparency and robustness of models and numerical code, in simulating the appropriate scenarios via explicitly identifying underlying assumptions about the process in nature and simplifying approximations to facilitate modelling, in correctly quantifying the uncertainty of the model parameters and projections, in taking into account the variable quality of data sources, and applying established software engineering practices to avoid duplication of effort and ensure reproducibility of numerical results. Via a collection of 16 technical papers, this special issue aims to address some of these challenges alongside showcasing the usefulness of modelling as applied in this pandemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Keywords: COVID-19; epidemics; mathematical modelling.

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Figures

Figure 1.
Figure 1.
Conceptual map of the articles in this issue. Each article fits into at least one of these subject areas. Where an article treats more than one subject, an edge is drawn between them.
See this image and copyright information in PMC

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References

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