Aarne Pohjonen; Kolmogorov-Johnson-Mehl-Avrami model fitted to early COVID-19 mainland China infection outbreak data. AIP Conf. Proc. 28 September 2023; 2872 (1): 030005. https://doi.org/10.1063/5.0162935
Kolmogorov-Johnson-Mehl-Avrami model fitted to early COVID-19 mainland China infection outbreak data
1University of Oulu, Materials and Mechanical Engineering, Faculty of Technology Pentti Kaiteran Katu 1, 90570 Oulu, Finland
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231013140115
|Publish Date:|| 2024-09-23
In 2007 Avramov provided theoretical framework which suggests that the Kolmogorov-Johnson-Mehl-Avrami (KJMA) model, which is commonly used in materials science to describe transformation phenomena, could be used in describing infection spreading in human networks. In the current article the KJMA model is fitted to the COVID-19 mainland China infection data, which consists of 29 datasets for different regions. It was found that the model provided very good fit to the datasets. The obtained values for rate constant, Avrami exponent and the initiation time are provided for all of the cases.
AIP conference proceedings
11th International Conference on Mathematical Modeling in Physical Sciences
|Host publication editor:||
International Conference on Mathematical Modeling in Physical Sciences
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
The fitting scripts that were used in this work are freely available at . The up-to-date infection data is freely availble at .
© 2023 Authors. Published by AIP Publishing. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Aarne Pohjonen; Kolmogorov-Johnson-Mehl-Avrami model fitted to early COVID-19 mainland China infection outbreak data. AIP Conf. Proc. 28 September 2023; 2872 (1): 030005 and may be found at https://doi.org/10.1063/5.0162935.