The Generalized-Growth Modeling of COVID-19 - Wu, Ye; Zhang, Lin; Cao, Wenjing; Liu, Xiaofei; Feng, Xin (Frontiers in Physics, 2021)
The pandemic spreading of COVID-19 all over the world 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, referring 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.