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" A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects. "
Lu, Zhenzhen; Yu, Yongguang; Chen, YangQuan; Ren, Guojian; Xu, Conghui; Wang, Shuhui; Yin, Zhe
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AL
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911196
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LA1w8286m7
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| Title & Author
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A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects. [Article]\ Lu, Zhenzhen; Yu, Yongguang; Chen, YangQuan; Ren, Guojian; Xu, Conghui; Wang, Shuhui; Yin, Zhe
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2020
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| Title of Periodical
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UC Merced
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| Abstract
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In the end of 2019, a new type of coronavirus first appeared in Wuhan. Through the real-data of COVID-19 from January 23 to March 18, 2020, this paper proposes a fractional SEIHDR model based on the coupling effect of inter-city networks. At the same time, the proposed model considers the mortality rates (exposure, infection and hospitalization) and the infectivity of individuals during the incubation period. By applying the least squares method and prediction-correction method, the proposed system is fitted and predicted based on the real-data from January 23 to March 18-m where m represents predict days. Compared with the integer system, the non-network fractional model has been verified and can better fit the data of Beijing, Shanghai, Wuhan and Huanggang. Compared with the no-network case, results show that the proposed system with inter-city network may not be able to better describe the spread of disease in China due to the lock and isolation measures, but this may have a significant impact on countries that has no closure measures. Meanwhile, the proposed model is more suitable for the data of Japan, the USA from January 22 and February 1 to April 16 and Italy from February 24 to March 31. Then, the proposed fractional model can also predict the peak of diagnosis. Furthermore, the existence, uniqueness and boundedness of a nonnegative solution are considered in the proposed system. Afterward, the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number R0≤1 , which provide a theoretical basis for the future control of COVID-19.
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