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

In the face of this new infectious disease and its complicated features with many unknown factors, single model estimations may infer biased results; therefore, we tried to make overall rigorous estimations by comparing different model results. To achieve an objective estimation, we investigated and implemented the two most common approaches and one extended approach: the logistic model, the susceptible-exposed-infected-removed (SEIR) model and the adjusted SEIR model. Countries had different initial times and levels of interventions and measures to reduce the risk of domestic secondary infections of COVID-19. We compared the models that have taken these effects into account or not, predicted the spread of the epidemic, and tried to compare different recommendations from three different models.