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Modeling the Virus Infection at the Population Level.
Wu, Cong; Fan, Xuemeng; Tang, Tong; Shen, Bairong.
  • Wu C; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Fan X; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Tang T; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Shen B; Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan, China. bairong.shen@scu.edu.cn.
Adv Exp Med Biol ; 1368: 141-166, 2022.
Article in English | MEDLINE | ID: covidwho-1858953
ABSTRACT
As pointed out by many researchers in the last few decades, differential equations with fractional (non-integer) order differential operators, in comparison with classical integer order ones, have apparent advantages in modeling. A Caputo fractional order system of ordinary differential equations is introduced to model the virus infection at the population level in this chapter. As well known, there are two main methods to study the dynamics of a model qualitative analysis and numerical modeling. Here the qualitative analysis, including uniqueness, invariant set, and stability, is first presented with intuitive derivation. Then the famous genetic algorithm is introduced to numerically model the dynamics of virus infection, i.e. to adjust the parameters of the Caputo fractional model such that its solution can properly fit real data and predict future.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Virus Diseases / COVID-19 Type of study: Prognostic study / Qualitative research Limits: Humans Language: English Journal: Adv Exp Med Biol Year: 2022 Document Type: Article Affiliation country: 978-981-16-8969-7_7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Virus Diseases / COVID-19 Type of study: Prognostic study / Qualitative research Limits: Humans Language: English Journal: Adv Exp Med Biol Year: 2022 Document Type: Article Affiliation country: 978-981-16-8969-7_7