Forecasting the Worldwide Spread of COVID-19 based on Logistic Model and SEIR Model

Xiang Zhou; Na Hong; Yingying Ma; Jie He; Huizhen Jiang; Chun Liu; Guangliang Shan; Longxiang Su; Weiguo Zhu; Yun Long

With the number of cases growing in more than 150 countries and regions, modeling the transmission dynamics and estimating the development of COVID-19 are crucial to providing decisional support for public health departments and healthcare policy makers. Mathematical models are widely used in evaluating epidemic transmissions, forecasting the trend of disease spread, and providing optimal intervention strategies and control measures. A considerable number of recent studies have contended to estimate the scale and severity of COVID-19, and several mathematical models and predicting approaches have attempted to estimate the transmission of COVID-19 [4] [5] [6] [7] [8] . The majority of the studies have estimated the basic reproductive number R 0 , a key parameter to evaluate the potential for COVID-19 transmission. However, different models often yield different conclusions in terms of differences in model structure and input parameters. It is imperative and critical to improve the early predictive and warning capabilities of potential models for the pandemic.