Authors:
Includes more than 400 examples, figures, and problems
Provides problem sets, both analytical and computational
Teaches students how to integrate data and physics-based modeling
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (14 chapters)
-
Front Matter
-
Mathematical epidemiology
-
Front Matter
-
-
Computational epidemiology
-
Front Matter
-
-
Network epidemiology
-
Front Matter
-
-
Data-driven epidemiology
-
Front Matter
-
-
Back Matter
About this book
This innovative textbook brings together modern concepts in mathematical epidemiology, computational modeling, physics-based simulation, data science, and machine learning to understand one of the most significant problems of our current time, the outbreak dynamics and outbreak control of COVID-19. It teaches the relevant tools to model and simulate nonlinear dynamic systems in view of a global pandemic that is acutely relevant to human health.
If you are a student, educator, basic scientist, or medical researcher in the natural or social sciences, or someone passionate about big data and human health: This book is for you! It serves as a textbook for undergraduates and graduate students, and a monograph for researchers and scientists. It can be used in the mathematical life sciences suitable for courses in applied mathematics, biomedical engineering, biostatistics, computer science, data science, epidemiology, health sciences, machine learning, mathematical biology, numerical methods, and probabilistic programming. This book is a personal reflection on the role of data-driven modeling during the COVID-19 pandemic, motivated by the curiosity to understand it.
Keywords
- Data-driven modeling covid
- Data-driven modeling coronavirus
- epidemiology covid
- epidemiology coronavirus
- mathematical biology covid
- mathematical biology coronavirus
- Machine learning covid
- Machine learning coronavirus
- compartment modeling COVID19
- early outbreak dynamics COVID-19
- asymptomatic transmission COVID-19
- outbreak dynamics COVID-19
- change-point modeling COVID-19
- dynamic compartment modeling COVID-19
- network modeling epidemic
- network modeling COVID-19
- vaccination strategies covid
Reviews
“This is a very ambitious book. … Every chapter has a collection of very good problems. … the many examples using real data make the book a valuable resource. Overall the book presents a number of important ideas and offers some significant new approaches for modeling real and complicated epidemics. It’s a great place to begin to understand where mathematical epidemiology is now and where it has to go.” (Bill Satzer, MAA Reviews, May 9, 2022)
Authors and Affiliations
-
Mechanical Engineering, Stanford University, Stanford, USA
Ellen Kuhl
About the author
Ellen Kuhl is the Walter B. Reinhold Professor in the School of Engineering and Robert Bosch Chair of Mechanical Engineering at Stanford University. She is a Professor of Mechanical Engineering and, by courtesy, Bioengineering. She received her PhD from the University of Stuttgart in 2000 and her Habilitation from the University of Kaiserslautern in 2004. Her area of expertise is Living Matter Physics, the design of theoretical and computational models to simulate and predict the behavior of living systems. Ellen has published more than 200 peer-reviewed journal articles and edited two books; she is an active reviewer for more than 20 journals at the interface of engineering and medicine and an editorial board member of seven international journals in her field. She is a founding member of the Living Heart Project, a translational research initiative to revolutionize cardiovascular science through realistic simulation with 400 participants from research, industry, and medicine from 24 countries. Ellen is the current Chair of the US National Committee on Biomechanics and a Member-Elect of the World Council of Biomechanics. She is a Fellow of the American Society of Mechanical Engineers and of the American Institute for Mechanical and Biological Engineering. She received the National Science Foundation Career Award in 2010, was selected as Midwest Mechanics Seminar Speaker in 2014,
and received the Humboldt Research Award in 2016 and the ASME Ted Belytschko Applied Mechanics Award in 2021. Ellen is an All American triathlete on the Wattie Ink. Elite Team, a multiple Boston, Chicago, and New York marathon runner, and a Kona Ironman World Championship finisher.Bibliographic Information
Book Title: Computational Epidemiology
Book Subtitle: Data-Driven Modeling of COVID-19
Authors: Ellen Kuhl
DOI: https://doi.org/10.1007/978-3-030-82890-5
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-82889-9Published: 23 September 2021
Softcover ISBN: 978-3-030-82892-9Published: 24 September 2022
eBook ISBN: 978-3-030-82890-5Published: 22 September 2021
Edition Number: 1
Number of Pages: XVI, 312
Topics: Biomedical Engineering and Bioengineering, Bioinformatics, Epidemiology, Mathematical Modeling and Industrial Mathematics