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Understanding Epidemic Multi-wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group

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Mathematics of Public Health

Part of the book series: Fields Institute Communications ((FIC,volume 88))

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Abstract

Pandemics are becoming a recurring threat to our global society. Henceforth it is of paramount importance to provide reliable and simple mathematical modelling of their spread to guide decision makers and healthcare stakeholders. We review a novel symmetry-based analysis of epidemiological data, based on ideas adapted from theoretical physics, also known as the epidemiological Renormalization Group (eRG) framework. One major result is the first consistent mathematical modelling of multi-wave patterns, which have been observed in infectious disease spread. Thanks to the plethora of data available for COVID-19, we studied the evolution of variants via an unsupervised machine learning approach of the spike protein genome. With the use of the eRG framework, we confirmed that the emergence of new virus variants is one of the major causes of the onset of a new wave. This result can shape the best strategy to control and tame ongoing and future pandemics.

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Acknowledgements

We thank the members of the team that allowed us to obtain all the novel results summarized in this chapter. They comprise theoretical physicists F. Sannino, S. Hohenegger, M. della Morte, and C. Cot; experimental particle physicists F. Conventi, F. Cirotto, and A. Giannini; computer and data scientists M. Óskarsdóttir, A.S. Islind, and A. de Hoffer; and biologists M.L. Chiusano and A. Cimarelli.

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  1. Galileo Galilei Institute for Theoretical Physics, Firenze, Italy

    Shahram Vatani

  2. Institut de Physique des Deux Infinis de Lyon (IP2I), IN2P3/CNRS, Villeurbanne, France

    Giacomo Cacciapaglia

  3. Université Claude Bernard, University of Lyon, Lyon, France

    Giacomo Cacciapaglia

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  1. Shahram Vatani
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  1. Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada

    Jummy David

  2. Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON, Canada

    Jianhong Wu

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Vatani, S., Cacciapaglia, G. (2023). Understanding Epidemic Multi-wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group. In: David, J., Wu, J. (eds) Mathematics of Public Health. Fields Institute Communications, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-031-40805-2_3

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