Prediction of the epidemic trends of COVID-19 by the improved dynamic SEIR model

Jingyi Jiang, Lei Jiang, Gaorong Li, Jingxuan Luo, Meitang Wang, Haizhou Xu, Junhua Zhang

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Received date: 14th May 2020

The outbreak of 2019 novel coronavirus disease (COVID-19) has become a public health emergency of international concern. The purpose of this study was to propose an improved dynamic SEIR (ID-SEIR) model to predict the epidemic trends of novel COVID-19. Firstly, we obtain the values of parameters in ID-SEIR model by using the epidemic data of Wuhan as the training sample. Secondly, we predict the epidemic trends of COVID-19 for the three most serious USA, New York and Italy with our proposed ID-SEIR model, and we can apply the proposed method to predict the epidemic trends of other countries and areas. For USA, if the USA government takes some measures for infection prevention and control, the predicted cumulative infectious cases will reach a peak on August 14, and the predicted value is about 2,032,100 (95% CI: 1,988,800-2,075,500). But if the government does not consider taking these measures, the predicted cumulative infectious cases will be more than 3 million and the epidemic could not be controlled in August. For New York, the predicted cumulative infectious cases will reach a peak on July 13, and the predicted value is about 410,700 (95% CI: 403,500-417,900). But if New York does not take strong measures for infection prevention and control, the predicted cumulative infectious cases will be more than 600,000 and the epidemic could not be controlled in July. For Italy, the predicted cumulative infectious cases will reach a peak on June 1, and the predicted value is about 232,000 (95% CI: 230,200-233,900). Finally, we find that the proposed ID-SEIR model established in this paper has strong reliability, which can reasonably reflect the changes in national policies and public behavior during the epidemic. Also, this model can make predictions in line with the actual development of the epidemic and provide reference for infection prevention and control.

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This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.



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