The Generalized-Growth Modeling of COVID-19

• Main Authors: Ye Wu, Lin Zhang, Wenjing Cao, Xiaofei Liu, Xin Feng
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-01-01
• Series: Frontiers in Physics
• Subjects: nonlinear dynamics; complex system; COVID-19; phenomenological model; sub-exponential growth; sub-linear growth
• Online Access: https://www.frontiersin.org/articles/10.3389/fphy.2020.603001/full

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.