A time-dependent model of the transmission of COVID-19 variants dynamics using Hausdorff fractal derivative

Shiqian Nie and Xiaochun Lei

Physica A: Statistical Mechanics and its Applications, 2023, vol. 629, issue C

Abstract: The coronavirus disease 2019 variants (COVID-19 variants) pandemic is still wreaking havoc across the world; and has a key impact on our lives, but the dynamic evolution of COVID-19 variants is still obscure. Hausdorff fractal calculus provides a powerful method for fractal dynamics modeling in computational biology. This paper studies the applicability of Hausdorff fractal differential equations (HDE) in dynamics modeling and the alleviation of coronavirus for the first time. Scholars have generated amounts of instructive research about the COVID-19 variants, and lack of time dependence. This paper will develop a time-dependent Hausdorff derivative model which focuses on the modeling, applications, and data analysis for the COVID-19 variants pandemic spreading in the world. The model results show that the transmission, infection, and recovery dynamics follow the integral-order Susceptible, Exposed, Infectious, and Recovered (SEIR) model, and the evolution of the death toll follows the time HDE. In this paper, a modified SEIR model is proposed, denoted by Hausdorff fractal derivative SEIR (HSEIR) model. Then we apply the HSEIR model to predict the evolution of COVID-19 variants in Brazil, China, Germany, India, Italy, Japan, and South Africa. The model successfully captured the process of developing COVID-19 variants, which provides a theoretical basis for understanding the trend of the epidemic.

Keywords: COVID-19 variants; Spreading; HSEIR; Prediction; Epidemic (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007513

DOI: 10.1016/j.physa.2023.129196

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