Social distancing mediated generalized model to predict epidemic spread of COVID-19

The extensive proliferation of recent coronavirus (COVID-19), all over the world, is the outcome of social interactions through massive transportation, gatherings and population growth. To disrupt the widespread of COVID-19, a mechanism for social distancing is indispensable. Also, to predict the ef...

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Bibliographic Details
Published in:Nonlinear dynamics 2021-10, Vol.106 (2), p.1187-1195
Main Authors: Yasir, Kashif Ammar, Liu, Wu-Ming
Format: Article
Language:English
Subjects:
Automotive Engineering
Classical Mechanics
Control
Coronaviruses
COVID-19
Disease control
Dynamical Systems
Engineering
Epidemics
Growth models
Harmonic functions
Information flow
Mechanical Engineering
Original Paper
Population growth
Social distancing
Social factors
Social networks
Vibration
Viral diseases
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Summary:The extensive proliferation of recent coronavirus (COVID-19), all over the world, is the outcome of social interactions through massive transportation, gatherings and population growth. To disrupt the widespread of COVID-19, a mechanism for social distancing is indispensable. Also, to predict the effectiveness and quantity of social distancing for a particular social network, with a certain contagion, a generalized model is needed. In this manuscript, we propose a social distancing mediated generalized model to predict the pandemic spread of COVID-19. By considering growth rate as a temporal harmonic function damped with social distancing in generalized Richard model and by using the data of confirmed COVID-19 cases in China, USA and India, we find that, with time, the cumulative spread grows more rapidly due to weak social distancing as compared to the stronger social distancing, where it is explicitly decreasing. Furthermore, we predict the possible outcomes with various social distancing scenarios by considering highest growth rate as an initial state, and illustrate that the increase in social distancing tremendously decreases growth rate, even it tends to reach zero in lockdown regimes. Our findings not only provide epidemic growth scenarios as a function of social distancing but also provide a modified growth model to predict controlled information flow in any network.
ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06424-0