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|a UAMI
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|a Diekmann, O.,
|e author.
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|a Mathematical tools for understanding infectious diseases dynamics /
|c Odo Diekmann, Hans Heesterbeek, and Tom Britton.
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|a Mathematical tools for understanding infectious disease dynamics
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|a Princeton :
|b Princeton University Press,
|c ©2013.
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300 |
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|a 1 online resource (xiv, 502 pages) :
|b illustrations
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a Princeton series in theoretical and computational biology
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|a Includes bibliographical references (pages 491-496) and index.
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520 |
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|a "Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. Mathematical Tools for Understanding Infectious Disease Dynamics fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided. Covers the latest research in mathematical modeling of infectious disease epidemiology Integrates deterministic and stochastic approaches Teaches skills in model construction, analysis, inference, and interpretation Features numerous exercises and their detailed elaborations Motivated by real-world applications throughout "--
|c Provided by publisher.
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0 |
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|a Print version record.
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|t Frontmatter --
|t Contents --
|t Preface --
|t Part I. The bare bones: Basic issues in the simplest context --
|t Part II. Structured populations --
|t Part III. Case studies on inference --
|t Part IV. Elaborations --
|t Bibliography --
|t Index
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|a JSTOR
|b Books at JSTOR Demand Driven Acquisitions (DDA)
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|a JSTOR
|b Books at JSTOR Evidence Based Acquisitions
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|a JSTOR
|b Books at JSTOR All Purchased
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|a Epidemiology
|x Mathematical models
|v Congresses.
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|a Epidemiology
|x Mathematical models.
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|a Communicable diseases
|x Mathematical models.
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|a Epidemiology.
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|a Mathematical models.
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|a Communicable diseases.
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|a Epidemiology
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|a Models, Theoretical
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|a Communicable Diseases
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|a Épidémiologie
|x Modèles mathématiques
|v Congrès.
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|a Épidémiologie
|x Modèles mathématiques.
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|a Maladies infectieuses
|x Modèles mathématiques.
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|a Épidémiologie.
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|a Modèles mathématiques.
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|a Maladies infectieuses.
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|a mathematical models.
|2 aat
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|a SCIENCE
|x Life Sciences
|x Biology
|x General.
|2 bisacsh
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|
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|a MATHEMATICS
|x Applied.
|2 bisacsh
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|
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|a MEDICAL
|x Infectious Diseases.
|2 bisacsh
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|a MEDICAL
|x Epidemiology.
|2 bisacsh
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|
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|a MEDICAL
|x Health Risk Assessment.
|2 bisacsh
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|a Mathematical models
|2 fast
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|
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|a Epidemiology
|2 fast
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|a Communicable diseases
|2 fast
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|a Communicable diseases
|x Mathematical models
|2 fast
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|a Epidemiology
|x Mathematical models
|2 fast
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|a Electronic books.
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|a Conference papers and proceedings
|2 fast
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700 |
1 |
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|a Heesterbeek, Hans,
|d 1960-
|e editor.
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700 |
1 |
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|a Britton, Tom,
|e editor.
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776 |
0 |
8 |
|i Print version:
|a Diekmann, O.
|t Mathematical tools for understanding infectious diseases dynamics.
|d Princeton : Princeton University Press, ©2013
|z 9780691155395
|w (DLC) 2012012058
|w (OCoLC)788265597
|
830 |
|
0 |
|a Princeton series in theoretical and computational biology.
|
856 |
4 |
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|u https://jstor.uam.elogim.com/stable/10.2307/j.cttq9530
|z Texto completo
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