Computational Epidemiology : Data-Driven Modeling of COVID-19 - Ellen Kuhl

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Computational Epidemiology

Data-Driven Modeling of COVID-19

By: Ellen Kuhl

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Paperback | 24 September 2022

At a Glance

Paperback
330 Pages

Dimensions(cm)
23.5 x 15.49 x 1.75

Paperback

$109.00

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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.

Industry Reviews

"Right off the bat, let me say that I really enjoyed Kuhl's book. It is beautifully written, very engaging, and the topics and examples are thoughtfully chosen. ... This is a timely and truly wonderful book." (Anita T. Layton, SIAM Review, Vol. 64 (3), September, 2022)
"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)

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