Kolmogorov-Johnson-Mehl-Avrami model fitted to early COVID-19 mainland China infection outbreak data

Pohjonen, Aarne (2023-09-23)
 
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https://doi.org/10.1063/5.0162935

Pohjonen, Aarne
AIP Publishing
23.09.2023

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

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© 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.
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doi:https://doi.org/10.1063/5.0162935
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https://urn.fi/URN:NBN:fi-fe20231013140115
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Abstract

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.

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