The Generalized-Growth Modeling of COVID-19
The global spread of the COVID-19 pandemic is changing everything in 2020. It is of crucial importance to characterize the growth patterns during the transmission. In this paper, a generalized-growth model is established to present the evolution of the number of the total confirmed cases changing wi...
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doaj-8923a0e0cdbd4b039b1c1284fa12d88d2021-05-18T07:50:37ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-01-01810.3389/fphy.2020.603001603001The Generalized-Growth Modeling of COVID-19Ye Wu0Lin Zhang1Wenjing Cao2Xiaofei Liu3Xin Feng4Xin Feng5School of Information Engineering, Putian University, Putian, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Economics and Management, Yanshan University, Qinhuangdao, ChinaSchool of Management Science and Engineering, Hebei GEO University, Shijiazhuang, ChinaThe global spread of the COVID-19 pandemic is changing everything in 2020. It is of crucial importance to characterize the growth patterns during the transmission. In this paper, a generalized-growth model is established to present the evolution of the number of the total confirmed cases changing with time. Due to effective containment, the generalized growth model reveals a piecewise pattern, referred to as the sub-exponential and the sub-linear stages. Moreover, the parameters can quantify the effectiveness of the containment and the trend of resurgence in different regions all over the world. Our model provides a phenomenological approach, which is simple and transparent for better understanding of the typical patterns within the general dynamics. Our model may have implications for possible nowcasting and forecasting of the pandemic trend.https://www.frontiersin.org/articles/10.3389/fphy.2020.603001/fullnonlinear dynamicscomplex systemCOVID-19phenomenological modelsub-exponential growthsub-linear growth |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ye Wu Lin Zhang Wenjing Cao Xiaofei Liu Xin Feng Xin Feng |
spellingShingle |
Ye Wu Lin Zhang Wenjing Cao Xiaofei Liu Xin Feng Xin Feng The Generalized-Growth Modeling of COVID-19 Frontiers in Physics nonlinear dynamics complex system COVID-19 phenomenological model sub-exponential growth sub-linear growth |
author_facet |
Ye Wu Lin Zhang Wenjing Cao Xiaofei Liu Xin Feng Xin Feng |
author_sort |
Ye Wu |
title |
The Generalized-Growth Modeling of COVID-19 |
title_short |
The Generalized-Growth Modeling of COVID-19 |
title_full |
The Generalized-Growth Modeling of COVID-19 |
title_fullStr |
The Generalized-Growth Modeling of COVID-19 |
title_full_unstemmed |
The Generalized-Growth Modeling of COVID-19 |
title_sort |
generalized-growth modeling of covid-19 |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Physics |
issn |
2296-424X |
publishDate |
2021-01-01 |
description |
The global spread of the COVID-19 pandemic is changing everything in 2020. It is of crucial importance to characterize the growth patterns during the transmission. In this paper, a generalized-growth model is established to present the evolution of the number of the total confirmed cases changing with time. Due to effective containment, the generalized growth model reveals a piecewise pattern, referred to as the sub-exponential and the sub-linear stages. Moreover, the parameters can quantify the effectiveness of the containment and the trend of resurgence in different regions all over the world. Our model provides a phenomenological approach, which is simple and transparent for better understanding of the typical patterns within the general dynamics. Our model may have implications for possible nowcasting and forecasting of the pandemic trend. |
topic |
nonlinear dynamics complex system COVID-19 phenomenological model sub-exponential growth sub-linear growth |
url |
https://www.frontiersin.org/articles/10.3389/fphy.2020.603001/full |
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