Research Article Open Access

Probabilistic Mechanics of Glass Suggests a Model for COVID-19 Epidemics

Gabriele Pisano1, Antonio Bonati1 and Gianni Royer Carfagni1,2
  • 1 Institute for Construction Technologies, National Research Council, Italy
  • 2 Department of Engineering and Architecture, University of Parma, Italy

Abstract

The presented statistical model allows us to identify the degree of development of an epidemic starting from the observation of the number of deaths in the invested region, categorized by the age of the victims. Recognition of which cases are associated with the disease is not necessary, as this results from the comparison with data on deaths in pre-epidemic conditions. The treatment, which has analogies with consolidated models in the stochastic mechanics of brittle materials, allows associating parameters such as the level of epidemic, the probability of developing a pathology, and the age of the victims, with other notions well-known to structural engineers, such as the stress, the dependence from fracture-mechanics of macroscopic strength on material defects, the size-effect. Strong simplifying hypotheses are made; therefore, new comparisons are needed with the actual data, often not organized and difficult to find. However, for the most populated Italian regions, the model shows a good agreement with the theoretical predictions, allowing a quantitative estimate of the epidemic level and the risk assessment based on age. Further studies will show whether the knowledge in material science may be conveniently borrowed in the field of epidemiology and vice-versa, to achieve mutual interdisciplinary progress.

International Journal of Structural Glass and Advanced Materials Research
Volume 6 No. 1, 2022, 33-47

DOI: https://doi.org/10.3844/sgamrsp.2022.33.47

Submitted On: 2 September 2022 Published On: 26 January 2023

How to Cite: Pisano, G., Bonati, A. & Carfagni, G. R. (2022). Probabilistic Mechanics of Glass Suggests a Model for COVID-19 Epidemics. International Journal of Structural Glass and Advanced Materials Research, 6(1), 33-47. https://doi.org/10.3844/sgamrsp.2022.33.47

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Keywords

  • Probabilistic Mechanics
  • Glass
  • COVID-19
  • Mathematical Epidemiology
  • Weibull Statistics