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Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact -

Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact (eBook)

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2021 | 1. Auflage
XXIV, 627 Seiten
Springer Singapore (Verlag)
978-981-16-2450-6 (ISBN)
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This edited volume is a collection of selected research articles discussing the analysis of infectious diseases by using mathematical modelling in recent times. Divided into two parts, the book gives a general and country-wise analysis of Covid-19. Analytical and numerical techniques for virus models are presented along with the application of mathematical modelling in the analysis of their spreading rates and treatments. The book also includes applications of fractional differential equations as well as ordinary, partial and integrodifferential equations with optimization methods. Probability distribution and their bio-mathematical applications have also been studied. This book is a valuable resource for researchers, scholars, biomathematicians and medical experts.



PRAVEEN AGARWAL is Associate Professor at the Department of Mathematics, Anand International College of Engineering, Jaipur, India. He completed his Ph.D. in Mathematics from the Malviya National Institute of Technology, Jaipur, India. His areas of research include special functions, fractional calculus, numerical analysis, differential and difference equations, inequalities, and fixed point theorems. He has authored 7 research monographs and edited volumes and over 150 research articles with approximately 100 mathematicians from around the world in prestigious national and international journals. He has delivered invited talks in universities and institutions around the world as well as guided various research students over the years. He is the recipient of the Most Outstanding Researcher (2018) award for his outstanding contribution to mathematics by the then Union Minister of Human Resource Development of India, Prakash Javadekar.

JUAN J. NIETO is Professor of Mathematical Analysis at the University of Santiago de Compostela, Spain, since 1991 and a fellow of the Royal Galician Academy of Sciences. He received his Ph.D. in Mathematics from the University of Santiago de Compostela, Spain, in 1983. His most influential contributions to date are in the area of differential equations, and his research interests are in fractional calculus, fuzzy equations and epidemiological models. He is one of the most cited mathematicians in the world according to the Web of Knowledge and appears in the Thompson Reuters Highly Cited Researchers list. His works have been published in various journals and conference proceedings of repute.

MICHAEL RUZHANSKY is Senior Full Professor at the Department of Mathematics, Ghent University, Belgium, and Professor at the School of Mathematical Sciences, Queen Mary University of London, UK. He received his Ph.D. in Mathematics from Utrecht University, The Netherlands, in 1998, with the thesis titled 'Singular Fibrations with Affine Fibers, with Applications to the Regularity Properties of Fourier Integral Operators.' He completed his M.Sc. in Mathematics and Education at St. Petersburg State University, Russia, in 1995, with the thesis titled 'Linear Optimal Filtering Theory with the Generalized Quadratic Quality Functional.' He is a recipient of several awards, including Ferran Sunyer I Balaguer Prize (2018), Odysseus I Project (2018), Ferran Sunyer I Balaguer Prize (2014) and Daiwa Adrian Prize (2010).

DELFIM F. M. TORRES is Full Professor of Mathematics at the Department of Mathematics, the University of Aveiro (UA), Portugal. He received his Ph.D. in mathematics from the University of Aveiro, in 2002. He is also the Director of the FCT Doctoral Programme Consortium in Mathematics and Applications (MAP-PDMA) of the University of Minho, Portugal, and the University of Beira Interior, Portugal. His main research areas are calculus of variations and optimal control, optimization with emphasis on the regularity of solutions and necessary optimality conditions, fractional derivatives and integrals, dynamic equations on time scales or measure chains, and mathematical biology. He has published over 400 scientific and pedagogical publications, including research papers in reputed international journals, refereed conference proceedings, chapters in books, and books (as an author and editor). Having guided 18 Ph.D. Torres has a strong experience in graduate and postgraduate student supervision and teaching mathematics, both in Portugal and abroad. Moreover, he has led teams and has been a member of several national and international R&D projects, including EU projects and networks. He was a key scientist of the European Marie Curie Project SADCO (Sensitivity Analysis for Deterministic Controller Design), Network for Initial Training, under the 7th Framework Programme FP7-PEOPLE-2010-ITN.


This edited volume is a collection of selected research articles discussing the analysis of infectious diseases by using mathematical modelling in recent times. Divided into two parts, the book gives a general and country-wise analysis of Covid-19. Analytical and numerical techniques for virus models are presented along with the application of mathematical modelling in the analysis of their spreading rates and treatments. The book also includes applications of fractional differential equations as well as ordinary, partial and integrodifferential equations with optimization methods. Probability distribution and their bio-mathematical applications have also been studied. This book is a valuable resource for researchers, scholars, biomathematicians and medical experts.
Erscheint lt. Verlag 29.9.2021
Reihe/Serie Infosys Science Foundation Series
Infosys Science Foundation Series in Mathematical Sciences
Zusatzinfo XXIV, 627 p. 253 illus., 236 illus. in color.
Sprache englisch
Themenwelt Sachbuch/Ratgeber Gesundheit / Leben / Psychologie Krankheiten / Heilverfahren
Mathematik / Informatik Mathematik Angewandte Mathematik
Studium Querschnittsbereiche Prävention / Gesundheitsförderung
Schlagworte Agent-based Models • Covid-19 • Dynamical Models • epidemic modelling • Machine Learning Models • Mathematical epidemiology • model prediction • pandemic influenza • Reproductive Number • Statistical Models
ISBN-10 981-16-2450-X / 981162450X
ISBN-13 978-981-16-2450-6 / 9789811624506
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