About: A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects

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About: A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects
Creator	Chen, Y Lu, Z Wang, · Yin, · Ren, G Yu, · Conghui, Ren Guojian, Chen Lu, Zhenzhen Shuhui, Xu Xu, · Yangquan, Yu Yongguang, · Zhe, Wang
Source	ArXiv
abstract	In this paper, a mathematical model is proposed to analyze the dynamic behavior of COVID-19. Based on inter-city networked coupling effects, a fractional-order SEIHDR system with the real-data from 23 January to 18 March, 2020 of COVID-19 is discussed. Meanwhile, hospitalized individuals and the mortality rates of three types of individuals (exposed, infected and hospitalized) are firstly taken into account in the proposed model. And infectivity of individuals during incubation is also considered in this paper. By applying least squares method and predictor-correctors scheme, the numerical solutions of the proposed system in the absence of the inter-city network and with the inter-city network are stimulated by using the real-data from 23 January to $18-m$ March, 2020 where $m$ is equal to the number of prediction days. Compared with integer-order system ($/alpha=0$), the fractional-order model without network is validated to have a better fitting of the data on Beijing, Shanghai, Wuhan, Huanggang and other cities. In contrast to the case without network, the results indicate that the inter-city network system may be not a significant case to virus spreading for China because of the lock down and quarantine measures, however, it may have an impact on cities that have not adopted city closure. Meanwhile, the proposed model better fits the data from 24 February to 31, March in Italy, and the peak number of confirmed people is also predicted by this fraction-order model. Furthermore, the existence and uniqueness of a bounded solution under the initial condition are considered in the proposed system. Afterwards, the basic reproduction number $R_0$ is analyzed and it is found to hold a threshold: the disease-free equilibrium point is locally asymptotically stable when $R_0/le 1$, which provides a theoretical basis for whether COVID-19 will become a pandemic in the future.
has issue date	2020-04-26(xsd:dateTime)
has license	arxiv
sha1sum (hex)	4df7ec80a84385b30b85a0432b1a770fd4671d92
resource representing a document's title	A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects
resource representing a document's body	covid:4df7ec80a84385b30b85a0432b1a770fd4671d92#body_text
is schema:about of	named entity 'Shanghai' named entity 'COVID-19' named entity 'number' named entity 'China' named entity 'fitting' named entity 'networked' named entity 'COVID-19' named entity 'NUMERICAL' named entity 'QUARANTINE' named entity 'ABSENCE OF' named entity 'PREDICTED' named entity 'LOCK' named entity 'VALIDATED' named entity 'APPLYING' named entity 'BOUNDED' named entity 'DESIGNATED' named entity 'COMPARED' named entity 'DATA' named entity 'THE PEAK' named entity 'JANUARY' named entity 'MATHEMATICAL MODEL' named entity 'WUHAN' named entity 'SPREADING' named entity 'THEORETICAL' named entity 'SIGNIFICANT' named entity 'coronavirus' named entity 'system' named entity 'February' named entity 'pandemic' named entity 'mortality rates' named entity 'hospitalized' named entity 'system' named entity 'scheme' named entity 'system' named entity 'fractional-order' named entity 'Shanghai' named entity 'COVID-19' named entity 'Huanggang' named entity 'Beijing' named entity 'infectivity' named entity 'fractional-order' named entity 'fractional-order' named entity 'COVID-19' named entity 'quarantine' named entity 'dynamic behavior' named entity 'COVID-19' named entity 'fractional-order' named entity 'infection' named entity 'Huanggang' named entity 'incubation period' named entity 'Wuhan' named entity '23 January' named entity 'incubation period' named entity 'COVID-19' named entity 'equilibrium point' named entity 'virus' named entity 'Italy' named entity 'COVID' named entity 'infectious diseases' named entity 'COVID' named entity 'North America' named entity 'China' named entity 'Hubei' named entity 'S 1' named entity 'basic reproduction number' named entity 'Wuhan' named entity 'Hubei' named entity 'people's Republic of China' named entity 'COVID-19' named entity 'COVID' named entity 'mortality rate' named entity 'Beijing' named entity 'boundary conditions' named entity 'insect'

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