Multi-scale phenomena behind the transmission of infectious disease: Comment on <i>“Data-driven mathematical modelling approaches for COVID-19: A survey” </i>by J. Demongeot &amp; P. Magal

Abstract

In the very interesting review article by Demongeot and Magal the authors overviewed a variety of data-driven phenomenological models derived over the past four years (by the authors themselves, their collaborators, and by other researchers) to investigate the evolution of reported and unreported cases of COVID-19. These models, which could be used to describe the data without the mechanistic description of the processes involved in the phenomenon, were employed to forecast the dynamics of single and multiple epidemic waves, as well as the dynamics of infections when considering or not the importance of age structure. By starting the review with a very brief historical overview of some main developments in epidemiology, the authors emphasised from the beginning the historical importance played by the collection of data, to shape the interventions aimed at reducing the transmission of infections. In addition to this discussion on the historical importance of data and the historical development of phenomenological models to reproduce such data, the presentation of various data sets for the daily and the cumulative number of COVID-19 infections across different countries and different time periods, provided a very convincing evidence for the usefulness of these minimal phenomenological models in epidemiology.

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DOI

https://doi.org/10.1016/j.plrev.2024.12.001

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https://discovery.dundee.ac.uk/en/publications/293ad226-320c-461a-bbaf-2aa7f6e69699