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Differential Equations and Population Dynamics I = Introductory Approaches /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Differential Equations and Population Dynamics I/ by Arnaud Ducrot, Quentin Griette, Zhihua Liu, Pierre Magal.
Reminder of title:
Introductory Approaches /
Author:
Ducrot, Arnaud.
other author:
Griette, Quentin.
Description:
XX, 458 p.online resource. :
Contained By:
Springer Nature eBook
Subject:
Mathematics. -
Online resource:
https://doi.org/10.1007/978-3-030-98136-5
ISBN:
9783030981365
Differential Equations and Population Dynamics I = Introductory Approaches /
Ducrot, Arnaud.
Differential Equations and Population Dynamics I
Introductory Approaches /[electronic resource] :by Arnaud Ducrot, Quentin Griette, Zhihua Liu, Pierre Magal. - 1st ed. 2022. - XX, 458 p.online resource. - Lecture Notes on Mathematical Modelling in the Life Sciences,2193-4797. - Lecture Notes on Mathematical Modelling in the Life Sciences,.
Part I Linear Differential and Difference Equations: 1 Introduction to Linear Population Dynamics -- 2 Existence and Uniqueness of Solutions -- 3 Stability and Instability of Linear Systems -- 4 Positivity and Perron-Frobenius Theorem -- Part II NonLinear Differential: 5 Nonlinear Differential Equation -- 6 The Linearized Stability Principle and the Hartman–Grobman Theorem -- 7 Positivity and Invariant Sub-Regions -- 8 Monotone Semiflows -- Part III Applications to Epidemic Models: 9 Understanding and Predicting Unreported Cases in the 2019-nCov Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions -- 10 The COVID-19 Outbreak in Japan: Unreported Age-Dependent Cases -- 11 Clarifying Predictions for COVID-19 from Testing Data: The Example of New York State -- 12 SI Epidemic Model Applied to COVID-19 Data in Mainland China -- 13 A Robust Phenomenological Approach to Investigating COVID-19 Data for France -- 14 What Can We Learn From COVID-19 Data By Using Epidemic Models With Unidentified Infectious Cases? -- 15 Supplementary material.
This book provides an introduction to the theory of ordinary differential equations and its applications to population dynamics. Part I focuses on linear systems. Beginning with some modeling background, it considers existence, uniqueness, stability of solution, positivity, and the Perron–Frobenius theorem and its consequences. Part II is devoted to nonlinear systems, with material on the semiflow property, positivity, the existence of invariant sub-regions, the Linearized Stability Principle, the Hartman–Grobman Theorem, and monotone semiflow. Part III opens up new perspectives for the understanding of infectious diseases by applying the theoretical results to COVID-19, combining data and epidemic models. Throughout the book the material is illustrated by numerical examples and their MATLAB codes are provided. Bridging an interdisciplinary gap, the book will be valuable to graduate and advanced undergraduate students studying mathematics and population dynamics.
ISBN: 9783030981365
Standard No.: 10.1007/978-3-030-98136-5doiSubjects--Topical Terms:
527692
Mathematics.
LC Class. No.: T57-57.97
Dewey Class. No.: 519
Differential Equations and Population Dynamics I = Introductory Approaches /
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Part I Linear Differential and Difference Equations: 1 Introduction to Linear Population Dynamics -- 2 Existence and Uniqueness of Solutions -- 3 Stability and Instability of Linear Systems -- 4 Positivity and Perron-Frobenius Theorem -- Part II NonLinear Differential: 5 Nonlinear Differential Equation -- 6 The Linearized Stability Principle and the Hartman–Grobman Theorem -- 7 Positivity and Invariant Sub-Regions -- 8 Monotone Semiflows -- Part III Applications to Epidemic Models: 9 Understanding and Predicting Unreported Cases in the 2019-nCov Epidemic Outbreak in Wuhan, China, and the Importance of Major Public Health Interventions -- 10 The COVID-19 Outbreak in Japan: Unreported Age-Dependent Cases -- 11 Clarifying Predictions for COVID-19 from Testing Data: The Example of New York State -- 12 SI Epidemic Model Applied to COVID-19 Data in Mainland China -- 13 A Robust Phenomenological Approach to Investigating COVID-19 Data for France -- 14 What Can We Learn From COVID-19 Data By Using Epidemic Models With Unidentified Infectious Cases? -- 15 Supplementary material.
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