| 书目名称 | Computational Epidemiology | | 副标题 | Data-Driven Modeling | | 编辑 | Ellen Kuhl | | 视频video | | | 概述 | 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 | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Textbook 2021 | | 关键词 | Data-driven modeling covid; Data-driven modeling coronavirus; epidemiology covid; epidemiology coronavi | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-82890-5 | | isbn_softcover | 978-3-030-82892-9 | | isbn_ebook | 978-3-030-82890-5 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:55:55
