Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world

Ke Wu; Didier Darcet; Qian Wang; Didier Sornette

In this paper, we employ the logistic growth model, the generalized logistic growth model, the generalized growth model and the generalized Richards model, which have been successfully applied to describe previous epidemics [16] [17] [18] [19] [20] . All these models have some limitations and are only applicable in some stages of the outbreak, or when enough data points are available. Thus, we first calibrate different models to the reported number of infected cases in the COVID-19 epidemics from Jan. 19 to March 10 for the whole of China and 29 provinces in mainland China, and then draw some lessons useful to interpret the results of a similar modeling exercise performed on the four countries that are undergoing major outbreaks of this virus: Japan, South Korea, Iran, and Italy. Our analysis dissects the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. Borrowing from the experience of China, we made projections on the development of the outbreak in the four key countries and the whole Europe, based on different scenarios provided by the results from different models. Our study employs simple models to quantitatively document the effects of the Chinese containment measures against the COVID-19, and provide informative implications for the coming pandemic.