About: Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model

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About: Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model 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	Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model
Creator	Chen, Yangquan Lu, Zhenzhen Mechatronics, Xu, Conghui Yongguang, Conghui Yu, Yongguang Zhenzhen, Yu
topic	covid:68b1aaf54cdad74b9b4095f253802a7a191a2ee6#this
Source	ArXiv
abstract	In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which has a basic guiding significance for the prediction of the possible outbreak of infectious diseases like COVID-19 and other insect diseases in the future. Firstly, some qualitative properties of the model are analyzed. The basic reproduction number $R_{0}$ is derived. When $R_{0}<1$, the disease-free equilibrium point is unique and locally asymptotically stable. When $R_{0}>1$, the endemic equilibrium point is also unique. Furthermore, some conditions are established to ensure the local asymptotic stability of disease-free and endemic equilibrium points. The trend of COVID-19 spread in the United States is predicted. Considering the influence of the individual behavior and government mitigation measurement, a modified SEIQRP model is proposed, defined as SEIQRPD model. According to the real data of the United States, it is found that our improved model has a better prediction ability for the epidemic trend in the next two weeks. Hence, the epidemic trend of the United States in the next two weeks is investigated, and the peak of isolated cases are predicted. The modified SEIQRP model successfully capture the development process of COVID-19, which provides an important reference for understanding the trend of the outbreak.
has issue date	2020-04-27(xsd:dateTime)
has license	arxiv
sha1sum (hex)	68b1aaf54cdad74b9b4095f253802a7a191a2ee6
resource representing a document's title	Forecast analysis of the epidemics trend of COVID-19 in the United States by a generalized fractional-order SEIR model
resource representing a document's body	covid:68b1aaf54cdad74b9b4095f253802a7a191a2ee6#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/68b1aaf54cdad74b9b4095f253802a7a191a2ee6
is schema:about of	named entity 'prediction' named entity 'properties' named entity 'SEIR' named entity 'guiding' named entity 'isolated' named entity 'outbreak' named entity 'ANALYZED' named entity 'INDIVIDUAL BEHAVIOR' named entity 'MEASUREMENT' named entity 'INFECTIOUS DISEASES' named entity 'TREND' named entity 'CAPTURE' named entity 'PROCESS' named entity 'EPIDEMIC' named entity 'PROPOSED' named entity 'CONDITIONS' named entity 'ISOLATED CASES' named entity 'DATA' named entity 'CONSIDERING' named entity 'MITIGATION' named entity 'FUTURE' named entity 'behavior' named entity 'data' named entity 'model' named entity 'When' named entity 'model' named entity 'asymptotic stability' named entity 'United States' named entity 'epidemic' named entity 'cases' named entity 'generalized' named entity 'diseases' named entity 'United States' named entity 'asymptotic stability' named entity 'infectious diseases' named entity 'basic reproduction number' named entity 'United States' named entity 'epidemic' named entity 'asymptotically stable' named entity 'fractional-order' named entity 'COVID-19 in the United States' named entity 'epidemics' named entity 'development' named entity 'United States' named entity 'clinical symptoms' named entity 'April 20, 2020' named entity 'basic reproduction number' named entity 'asymptotically stable' named entity 'SEIR' named entity 'United States' named entity 'April 20, 2020' named entity 'COVID-19' named entity 'Hunan' named entity 'Science and Engineering' named entity 'COVID-19' named entity 'infection' named entity 'incidence rate' named entity 'equilibrium point' named entity 'Johns Hopkins University' named entity 'incidence rate' named entity 'equilibrium point' named entity 'United States' named entity 'incubation period' named entity '95%' named entity 'eigenvalues' named entity 'equilibrium point' named entity 'April 20, 2020' named entity 'United States' named entity 'Mittag-Leffler function' named entity 'COVID-19' named entity 'Johns Hopkins University' named entity 'COVID-19' named entity 'coronavirus' named entity 'epidemiological data' named entity 'time interval' named entity 'SEIR' named entity 'COVID-19' named entity 'coronavirus' named entity 'outbreak of the coronavirus'

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