Tracking and forecasting milepost moments of the epidemic in the early-outbreak: framework and applications to the COVID-19

Huiwen Wang; Yanwen Zhang; Shan Lu; Shanshan Wang

Recently, lots of literatures are related to the trend prediction of the COVID-19 in China. Zeng et al. (2020) proposed a multi-model ordinary differential equation set neural network and model-free methods to predict the interprovincial transmissions in mainland China, especially those from Hubei Province, and predicted that the COVID-19 in China is likely to decelerate before Feb 18th and to end before April 2020. Chen et al. (2020) made prediction based on epidemiological surveys and analyses, which showed that the total number of diagnoses would be 2-3 times that of SARS, and the peak is predicted to be in early or middle February. Yu et al. (2020) revised the SIR model based on the characteristics of the COVID-19 epidemic development, and proposed a time-varying parameter-SIR model to study the trend of the number of infected people. used the SEIR method to predict the end of the epidemic in most cities in mainland China. used the Markov chain Monte Carlo method to estimate R 0 , and inferred from the SEIR model that the peak COVID in Wuhan would be reached in April, and other cities in China would be delayed by 1 to 2 weeks.