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001 978-3-319-97487-3
003 DE-He213
005 20200812132600.0
007 cr nn 008mamaa
008 181030s2018 gw | s |||| 0|eng d
020 _a9783319974873
_9978-3-319-97487-3
024 7 _a10.1007/978-3-319-97487-3
_2doi
040 _cCUS
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMED090000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aMBNS
_2thema
082 0 4 _a519.5
_223
100 1 _aBjørnstad, Ottar N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aEpidemics
_h[electronic resource] :
_bModels and Data using R /
_cby Ottar N. Bjørnstad.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 312 p. 130 illus., 68 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aUse R!,
_x2197-5736
505 0 _aChapter 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 _aThis 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 _aStatistics .
650 0 _aEpidemiology.
650 0 _aInfectious diseases.
650 1 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17030
650 2 4 _aEpidemiology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/H63000
650 2 4 _aInfectious Diseases.
_0https://scigraph.springernature.com/ontologies/product-market-codes/H33096
830 0 _aUse R!,
_x2197-5736
856 4 0 _uhttps://doi.org/10.1007/978-3-319-97487-3
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
942 _cEBK
999 _c206591
_d206591