The impact of a power law-induced memory effect on the SARS-CoV-2 transmission

Sk, Tahajuddin; Biswas, Santosh; Sardar, Tridip

doi:10.1016/j.chaos.2022.112790

Cited by 2 publications

(2 citation statements)

References 75 publications

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“…This study shows that the power-law and the stretched exponential function can well describe the geographical country-level distributions, in a time period from the early phase of this epidemic to June 2022 [13]. In addition to the geographical power-law distribution, a power-law growth pattern during the early phase of this epidemic was also observed [14][15][16][17]. The power-law pattern reveals important intrinsic dynamics of the COVID-19 spread process in the complex network of human society.…”

Section: Introductionsupporting

confidence: 51%

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Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

Liu,

Zheng

2023

PLoS ONE

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This paper conducts a systematic statistical analysis of the characteristics of the geographical empirical distributions for the numbers of both cumulative and daily confirmed COVID-19 cases and deaths at county, city, and state levels over a time span from January 2020 to June 2022. The mathematical heavy-tailed distributions can be used for fitting the empirical distributions observed in different temporal stages and geographical scales. The estimations of the shape parameter of the tail distributions using the Generalized Pareto Distribution also support the observations of the heavy-tailed distributions. According to the characteristics of the heavy-tailed distributions, the evolution course of the geographical empirical distributions can be divided into three distinct phases, namely the power-law phase, the lognormal phase I, and the lognormal phase II. These three phases could serve as an indicator of the severity degree of the COVID-19 pandemic within an area. The empirical results suggest important intrinsic dynamics of a human infectious virus spread in the human interconnected physical complex network. The findings extend previous empirical studies and could provide more strict constraints for current mathematical and physical modeling studies, such as the SIR model and its variants based on the theory of complex networks.

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Section: Introductionsupporting

confidence: 51%

“…Ikram et al [62] developed a stochastic SIVR model for COVID-19. Sk et al [17] introduced a completely new model which incorporated the power-law effect of the COVID-19 transmission process. Chang et al [63] introduced a metapopulation SEIR model using mobile phone data to explain inequities and inform reopening.…”

Section: Plos Onementioning

confidence: 99%

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

Liu,

Zheng

2023

PLoS ONE

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Deciphering Memory Patterns in Eco-Epidemiological systems through the lens of BaFOMS: A Bayesian Fractional Order Model Selection Method

Mestry,

Singh,

Rocklöv

et al. 2024

Preprint

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The evolution of eco-epidemiological systems is significantly influenced by the memory or previous history of the system. These non-Markovian dynamics are effectively modelled using fractional derivatives (FDs), incorporating memory kernels that reflect long-term or short-term memory characteristics in corresponding nonlinear fractional differential evolution equations. We introduce BaFOMS, a framework for identifying and selecting the optimal FD model for eco-epidemiological processes based on historical data. Specifically, we evaluate a class of fractional logistic growth models defined by their time correlation functions and determine the optimal model through Bayesian inference, selecting the one with the highest posterior probability. We also demonstrated BaFOMS efficiency in parameter estimation and forecasting, producing reliable results with quantified uncertainties. The method is shown to be robust across a range of eco-epidemiological datasets, offering computational efficiency and reliable inference about the evolution dynamics.

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The impact of a power law-induced memory effect on the SARS-CoV-2 transmission

Cited by 2 publications

References 75 publications

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

Deciphering Memory Patterns in Eco-Epidemiological systems through the lens of BaFOMS: A Bayesian Fractional Order Model Selection Method

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