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Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model
In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which divided the population into susceptible, exposed, infectious, quarantined, recovered and insusceptible individuals and has a basic guiding significance for the prediction of the possible outbreak of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487266/ https://www.ncbi.nlm.nih.gov/pubmed/32952299 http://dx.doi.org/10.1007/s11071-020-05946-3 |
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author | Xu, Conghui Yu, Yongguang Chen, YangQuan Lu, Zhenzhen |
author_facet | Xu, Conghui Yu, Yongguang Chen, YangQuan Lu, Zhenzhen |
author_sort | Xu, Conghui |
collection | PubMed |
description | In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which divided the population into susceptible, exposed, infectious, quarantined, recovered and insusceptible individuals and has a basic guiding significance for the prediction of the possible outbreak of infectious diseases like the coronavirus disease in 2019 (COVID-19) and other insect diseases in the future. Firstly, some qualitative properties of the model are analyzed. The basic reproduction number [Formula: see text] is derived. When [Formula: see text] , the disease-free equilibrium point is unique and locally asymptotically stable. When [Formula: see text] , 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 USA is predicted. Considering the influence of the individual behavior and government mitigation measurement, a modified SEIQRP model is proposed, defined as SEIQRPD model, which is divided the population into susceptible, exposed, infectious, quarantined, recovered, insusceptible and dead individuals. According to the real data of the USA, 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 USA in the next two weeks is investigated, and the peak of isolated cases is 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. |
format | Online Article Text |
id | pubmed-7487266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-74872662020-09-14 Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model Xu, Conghui Yu, Yongguang Chen, YangQuan Lu, Zhenzhen Nonlinear Dyn Original Paper In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which divided the population into susceptible, exposed, infectious, quarantined, recovered and insusceptible individuals and has a basic guiding significance for the prediction of the possible outbreak of infectious diseases like the coronavirus disease in 2019 (COVID-19) and other insect diseases in the future. Firstly, some qualitative properties of the model are analyzed. The basic reproduction number [Formula: see text] is derived. When [Formula: see text] , the disease-free equilibrium point is unique and locally asymptotically stable. When [Formula: see text] , 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 USA is predicted. Considering the influence of the individual behavior and government mitigation measurement, a modified SEIQRP model is proposed, defined as SEIQRPD model, which is divided the population into susceptible, exposed, infectious, quarantined, recovered, insusceptible and dead individuals. According to the real data of the USA, 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 USA in the next two weeks is investigated, and the peak of isolated cases is 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. Springer Netherlands 2020-09-14 2020 /pmc/articles/PMC7487266/ /pubmed/32952299 http://dx.doi.org/10.1007/s11071-020-05946-3 Text en © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper Xu, Conghui Yu, Yongguang Chen, YangQuan Lu, Zhenzhen Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title | Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title_full | Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title_fullStr | Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title_full_unstemmed | Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title_short | Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model |
title_sort | forecast analysis of the epidemics trend of covid-19 in the usa by a generalized fractional-order seir model |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487266/ https://www.ncbi.nlm.nih.gov/pubmed/32952299 http://dx.doi.org/10.1007/s11071-020-05946-3 |
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