Dynamic models for Coronavirus Disease 2019 and data analysis

Shao, Nian; Zhong, Min; Yan, Yue; Pan, HanShuang; Cheng, Jin; Chen, Wenbin

In this letter, two time delay dynamic models, a Time Delay Dynamical–Novel Coronavirus Pneumonia (TDD‐NCP) model and Fudan‐Chinese Center for Disease Control and Prevention (CCDC) model, are introduced to track the data of Coronavirus Disease 2019 (COVID‐19). The TDD‐NCP model was developed recently by Chengąŕs group in Fudan and Shanghai University of Finance and Economics (SUFE). The TDD‐NCP model introduced the time delay process into the differential equations to describe the latent period of the epidemic. The Fudan‐CDCC model was established when Wenbin Chen suggested to determine the kernel functions in the TDD‐NCP model by the public data from CDCC. By the public data of the cumulative confirmed cases in different regions in China and different countries, these models can clearly illustrate that the containment of the epidemic highly depends on early and effective isolations.