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Identifiability and observability in epidemiological models = a primer /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifiability and observability in epidemiological models/ by Nik Cunniffe ... [et al.].
Reminder of title:	a primer /
other author:	Cunniffe, Nik.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xii, 108 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction -- Chapter 2 Mathematical foundations -- Chapter 3 Analysis of the Kermack -- Chapter 4 McKendrick model -- Chapter 5 Observers synthesis -- Chapter 6 Practical and numerical considerations -- Chapter 7 Appendix -- Chapter 8 References.
Contained By:	Springer Nature eBook
Subject:	Epidemiology - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-981-97-2539-7
ISBN:	9789819725397

Identifiability and observability in epidemiological models = a primer /
Identifiability and observability in epidemiological modelsa primer /[electronic resource] :by Nik Cunniffe ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xii, 108 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs on PDEs and data science,2731-7609. - SpringerBriefs on PDEs and data science..

Chapter 1 Introduction -- Chapter 2 Mathematical foundations -- Chapter 3 Analysis of the Kermack -- Chapter 4 McKendrick model -- Chapter 5 Observers synthesis -- Chapter 6 Practical and numerical considerations -- Chapter 7 Appendix -- Chapter 8 References.

This book introduces the concepts of identifiability and observability in mathematical epidemiology, as well as those of observers' constructions. It first exposes and illustrates on several examples the mathematical definitions and properties of observability and identifiability. A chapter is dedicated to the well-known Kermack McKendrick model, for which the complete analysis of identifiability and observability is not available in the literature. Then, several techniques of observer constructions, in view of online estimation of state and parameters, are presented and deployed on several models. New developments relevant for applications in epidemiology are also given. Finally, practical considerations are discussed with data and numerical simulations related to models previously analysed in the book. The book will be appealing to epidemiological modellers and mathematicians working on models in epidemiology.This book contributes to Sustainable Development Goal 3 (SDG3): Good Health and Well Being.

ISBN: 9789819725397

Standard No.: 10.1007/978-981-97-2539-7doiSubjects--Topical Terms:

660489
Epidemiology
--Mathematical models.

LC Class. No.: RA652.2.M3

Dewey Class. No.: 614.4015118

Identifiability and observability in epidemiological models = a primer /
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245 0 0 $a Identifiability and observability in epidemiological models $h [electronic resource] : $b a primer / $c by Nik Cunniffe ... [et al.].
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300 $a xii, 108 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs on PDEs and data science, $x 2731-7609
505 0 $a Chapter 1 Introduction -- Chapter 2 Mathematical foundations -- Chapter 3 Analysis of the Kermack -- Chapter 4 McKendrick model -- Chapter 5 Observers synthesis -- Chapter 6 Practical and numerical considerations -- Chapter 7 Appendix -- Chapter 8 References.
520 $a This book introduces the concepts of identifiability and observability in mathematical epidemiology, as well as those of observers' constructions. It first exposes and illustrates on several examples the mathematical definitions and properties of observability and identifiability. A chapter is dedicated to the well-known Kermack McKendrick model, for which the complete analysis of identifiability and observability is not available in the literature. Then, several techniques of observer constructions, in view of online estimation of state and parameters, are presented and deployed on several models. New developments relevant for applications in epidemiology are also given. Finally, practical considerations are discussed with data and numerical simulations related to models previously analysed in the book. The book will be appealing to epidemiological modellers and mathematicians working on models in epidemiology.This book contributes to Sustainable Development Goal 3 (SDG3): Good Health and Well Being.
650 0 $a Epidemiology $x Mathematical models. $3 660489
650 1 4 $a Applications of Mathematics. $3 890893
650 2 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Epidemiology. $3 568544
650 2 4 $a Mathematical and Computational Biology. $3 1566274
700 1 $a Cunniffe, Nik. $3 3719468
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830 0 $a SpringerBriefs on PDEs and data science. $3 3632373
856 4 0 $u https://doi.org/10.1007/978-981-97-2539-7
950 $a Mathematics and Statistics (SpringerNature-11649)