The evolution of COVID-19: A discontinuous approach

Doménech‐Carbó, Antonio; Doménech-Casasús, Clara

doi:10.1016/j.physa.2021.125752

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…The characteristics of certain temporal process could be reflected from the pattern of spatial diffusion, for the spatial pattern and the temporal process depend on each other. The growth of confirmed cases of an epidemic usually appears as an S-shaped curve [ 4 , 38 ]. The simplest S-shaped curve is logistic curve.…”

Section: Discussionmentioning

confidence: 99%

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Gravitational scaling analysis on spatial diffusion of COVID-19 in Hubei Province, China

et al. 2021

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The spatial diffusion of epidemic disease follows distance decay law in geography and social physics, but the mathematical models of distance decay depend on concrete spatio-temporal conditions. This paper is devoted to modeling spatial diffusion patterns of COVID-19 stemming from Wuhan city to Hubei province, China. The modeling approach is to integrate analytical method and experimental method. The local gravity model is derived from allometric scaling and global gravity model, and then the parameters of the local gravity model are estimated by observational data and least squares calculation. The main results are as below. The local gravity model based on power law decay can effectively describe the diffusion patterns and process of COVID-19 in Hubei Province, and the goodness of fit of the gravity model based on negative exponential decay to the observational data is not satisfactory. Further, the goodness of fit of the model to data entirely became better and better over time, the size elasticity coefficient increases first and then decreases, and the distance attenuation exponent decreases first and then increases. Moreover, the significance of spatial autoregressive coefficient in the model is low, and the confidence level is less than 80%. The conclusions can be reached as follows. (1) The spatial diffusion of COVID-19 of Hubei bears long range effect, and the size of a city and the distance of the city to Wuhan affect the total number of confirmed cases. (2) Wuhan direct transmission is the main process in the spatial diffusion of COVID-19 in Hubei at the early stage, and the horizontal transmission between regions is not significant. (3) The effect of spatial lockdown and isolation measures taken by Chinese government against the transmission of COVID-19 is obvious. This study suggests that the role of urban gravity (size and distance) should be taken into account to prevent and control epidemic disease.

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Section: Discussionmentioning

confidence: 99%

“…The simplest S-shaped curve is logistic curve. The growth of confirmed cases of COVID-19 may increase exponentially in the short term [ 39 ], but in the long run it is logistic growth [ 38 ]. A process of logistic growth can be divided into three or four stages according to the velocity and acceleration [ 40 – 42 ].…”

Section: Discussionmentioning

confidence: 99%

Gravitational scaling analysis on spatial diffusion of COVID-19 in Hubei Province, China

et al. 2021

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“…This requires, for instance, a mathematical or computational model that is able to efficiently describe the complex growth profiles that arise in the cumulative epidemic curves and from which one can estimate the location and intensity of each wave’s peak in the daily curves. A standard way to investigate multiple-wave effects is to start with a basic epidemiological model and then allow its parameters to vary in time to reflect the occurrence of secondary waves of infections [ 3 , 4 ]. Understanding the evolution of possible multiple waves of infections is also, of course, relevant for public health officials, as it may help them to develop better strategies to fight the propagation of the virus.…”

Section: Introductionmentioning

confidence: 99%

Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics

et al. 2021

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We apply a generalized logistic growth model, with time-dependent parameters, to describe the fatality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. In the case of two waves only, the model parameters vary as a function of time according to a logistic function, whose two extreme values, i.e., for early and late times, characterize the first and second waves, respectively. For the multiple-wave model, the time-dependency of the parameters is likewise described by a multi-step logistic function with N intermediate plateaus, representing the N waves of the epidemic. We show that the theoretical curves are in excellent agreement with the empirical data for all countries considered here, namely: Brazil, Canada, Germany, Iran, Italy, Japan, Mexico, South Africa, Sweden, and the USA. The model also allows for predictions about the time of occurrence and severity of the subsequent waves. It is shown furthermore that the subsequent waves of COVID-19 can be generically classified into two main types, namely, standard and anomalous waves, according as to whether a given wave starts well after or well before the preceding one has subsided, respectively.

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“…This requires, for instance, a mathematical or computational model that is able to efficiently describe the complex growth profiles that arise in the cumulative epidemic curves and from which one can estimate the location and intensity of each wave’s peak in the daily curves. A standard way to investigate multiple-wave effects is to start with a basic epidemiological model and then allow its parameters to vary in time to reflect the occurrence of secondary waves of infections 3, 4 . Understanding the evolution of possible multiple waves of infections is also, of course, relevant for public health officials, as it may help them to develop better strategies to fight the propagation of the virus.…”

Section: Introductionmentioning

confidence: 99%

Standard and anomalous waves of COVID-19: A multiple-wave growth model for epidemics

Vasconcelos

Brum

Almeida

et al. 2021

Preprint

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We apply a generalised logistic growth model, with time dependent parameters, to describe the fatality curves of the COVID-19 disease for several countries that exhibit a second wave of infections. The model parameters vary as a function of time according to a logistic function, whose two extreme values, i.e., for early and late times, characterise the first and second waves, respectively. We show that the theoretical curves are in excellent agreement with the empirical data for all cases considered. The model also allows for predictions about the time of occurrence and relative severity of the second wave, in comparison to the first wave. It is shown furthermore that the COVID-19 second waves can be generically classified in two main types, namely, standard and anomalous second waves, according as to whether the second wave starts well after or still during the first wave, respectively. We have also observed that the standard second waves tend, in their majority, to be more severe than the corresponding first wave, whereas for anomalous second waves the opposite occurs.

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The evolution of COVID-19: A discontinuous approach

Cited by 6 publications

References 30 publications

Gravitational scaling analysis on spatial diffusion of COVID-19 in Hubei Province, China

Gravitational scaling analysis on spatial diffusion of COVID-19 in Hubei Province, China

Standard and Anomalous Waves of COVID-19: A Multiple-Wave Growth Model for Epidemics

Standard and anomalous waves of COVID-19: A multiple-wave growth model for epidemics

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