國立高雄大學圖資館 |

Language: English

Back

Epidemicsmodels and data using R /

Bjornstad, Ottar N.

Epidemicsmodels and data using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Epidemicsby Ottar N. Bjornstad.
Reminder of title:	models and data using R /
Author:	Bjornstad, Ottar N.
Published:	Cham :Springer International Publishing :2018.
Description:	xiii, 312 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	EpidemicsData processing.
Online resource:	https://doi.org/10.1007/978-3-319-97487-3
ISBN:	9783319974873$q(electronic bk.)

Epidemicsmodels and data using R /
Bjornstad, Ottar N.

Epidemicsmodels and data using R /[electronic resource] :by Ottar N. Bjornstad. - Cham :Springer International Publishing :2018. - xiii, 312 p. :ill. (some col.), digital ;24 cm. - Use R!,2197-5736. - Use R!..

Chapter 1. Introduction -- Chapter 2. SIR -- Chapter 3. R0 -- Chapter 4. FoI and age-dependent incidence -- Chapter 5. Seasonality -- Chapter 6. Time Series Analysis -- Chapter 7. TSIR -- Chapter 8 -- Trajectory Matching -- Chapter 9. Stability and Resonant Periodicity -- Chapter 10. Exotica -- Chapter 11. Spatial Dynamics -- Chapter 12. Transmission on Networks -- Chapter 13. Spatial and Spatiotemporal Patterns -- Chapter 14. Parasitoids -- Chapter 15. Non-Independent Data -- Chapter 16. Quantifying In-Host Patterns -- Bibliography -- Index.

This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in 'consumer-resource metapopulations'. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and 'models-with-data' have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and 'models-and-data' to understand epidemics and infectious disease dynamics in space and time.

ISBN: 9783319974873$q(electronic bk.)

Standard No.: 10.1007/978-3-319-97487-3doiSubjects--Topical Terms:

824926
Epidemics
--Data processing.

LC Class. No.: RA652.2.D38 / B567 2018

Dewey Class. No.: 614.4

Epidemicsmodels and data using R /
LDR:03416nmm a2200349 a 4500 001 545835
003 DE-He213
005 20190326150440.0
006 m d
007 cr nn 008maaau
008 190530s2018 gw s 0 eng d
020 $a 9783319974873$q(electronic bk.)
020 $a 9783319974866$q(paper)
024 7 $a 10.1007/978-3-319-97487-3 $2 doi
035 $a 978-3-319-97487-3
040 $a GP $c GP
041 0 $a eng
050 4 $a RA652.2.D38 $b B567 2018
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a MBNS $2 thema
082 0 4 $a 614.4 $2 23
090 $a RA652.2.D38 $b B626 2018
100 1 $a Bjornstad, Ottar N. $3 824925
245 1 0 $a Epidemics $h [electronic resource] : $b models and data using R / $c by Ottar N. Bjornstad.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiii, 312 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Use R!, $x 2197-5736
505 0 $a Chapter 1. Introduction -- Chapter 2. SIR -- Chapter 3. R0 -- Chapter 4. FoI and age-dependent incidence -- Chapter 5. Seasonality -- Chapter 6. Time Series Analysis -- Chapter 7. TSIR -- Chapter 8 -- Trajectory Matching -- Chapter 9. Stability and Resonant Periodicity -- Chapter 10. Exotica -- Chapter 11. Spatial Dynamics -- Chapter 12. Transmission on Networks -- Chapter 13. Spatial and Spatiotemporal Patterns -- Chapter 14. Parasitoids -- Chapter 15. Non-Independent Data -- Chapter 16. Quantifying In-Host Patterns -- Bibliography -- Index.
520 $a This book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in 'consumer-resource metapopulations'. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and 'models-with-data' have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and 'models-and-data' to understand epidemics and infectious disease dynamics in space and time.
650 0 $a Epidemics $x Data processing. $3 824926
650 0 $a Communicable diseases $x Data processing. $3 484586
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Epidemiology. $3 191577
650 2 4 $a Infectious Diseases. $3 274275
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Use R!. $3 797602
856 4 0 $u https://doi.org/10.1007/978-3-319-97487-3
950 $a Mathematics and Statistics (Springer-11649)