LEADER 04348nam 22005535 450 001 9910300128403321 005 20251116203845.0 010 $a3-319-97487-4 024 7 $a10.1007/978-3-319-97487-3 035 $a(CKB)4100000007110705 035 $a(DE-He213)978-3-319-97487-3 035 $a(MiAaPQ)EBC5628059 035 $a(PPN)23146178X 035 $a(EXLCZ)994100000007110705 100 $a20181030d2018 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEpidemics $eModels and Data using R /$fby Ottar N. Bjørnstad 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XIII, 312 p. 130 illus., 68 illus. in color.) 225 1 $aUse R!,$x2197-5736 311 08$a3-319-97486-6 327 $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.-. 330 $aThis book is designed to be a practical study in infectious disease dynamics. The book offers an easy to follow implementation and analysis of mathematical epidemiology. The book focuses on recent case studies in order to explore various conceptual, mathematical, and statistical issues. The dynamics of infectious diseases shows a wide diversity of pattern. Some have locally persistent chains-of-transmission, others persist spatially in ?consumer-resource metapopulations?. Some infections are prevalent among the young, some among the old and some are age-invariant. Temporally, some diseases have little variation in prevalence, some have predictable seasonal shifts and others exhibit violent epidemics that may be regular or irregular in their timing. Models and ?models-with-data? have proved invaluable for understanding and predicting this diversity, and thence help improve intervention and control. Using mathematical models to understand infectious disease dynamics has a very rich history in epidemiology. The field has seen broad expansions of theories as well as a surge in real-life application of mathematics to dynamics and control of infectious disease. The chapters of Epidemics: Models and Data using R have been organized in a reasonably logical way: Chapters 1-10 is a mix and match of models, data and statistics pertaining to local disease dynamics; Chapters 11-13 pertains to spatial and spatiotemporal dynamics; Chapter 14 highlights similarities between the dynamics of infectious disease and parasitoid-host dynamics; Finally, Chapters 15 and 16 overview additional statistical methodology useful in studies of infectious disease dynamics. This book can be used as a guide for working with data, models and ?models-and-data? to understand epidemics and infectious disease dynamics in space and time. 410 0$aUse R!,$x2197-5736 606 $aStatistics 606 $aEpidemiology 606 $aInfectious diseases 606 $aR (Computer program language) 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aEpidemiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H63000 606 $aInfectious Diseases$3https://scigraph.springernature.com/ontologies/product-market-codes/H33096 615 0$aStatistics. 615 0$aEpidemiology. 615 0$aInfectious diseases. 615 0$aR (Computer program language) 615 14$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aEpidemiology. 615 24$aInfectious Diseases. 676 $a519.5 700 $aBjørnstad$b Ottar N.$4aut$4http://id.loc.gov/vocabulary/relators/aut$0768238 906 $aBOOK 912 $a9910300128403321 996 $aEpidemics$91564721 997 $aUNINA LEADER 03895nam 22007455 450 001 9910522560303321 005 20251225203538.0 010 $a3-030-95470-6 024 7 $a10.1007/978-3-030-95470-3 035 $a(MiAaPQ)EBC6882567 035 $a(Au-PeEL)EBL6882567 035 $a(CKB)21069024600041 035 $a(DE-He213)978-3-030-95470-3 035 $a(PPN)260825360 035 $a(EXLCZ)9921069024600041 100 $a20220201d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning, Optimization, and Data Science $e7th International Conference, LOD 2021, Grasmere, UK, October 4?8, 2021, Revised Selected Papers, Part II /$fedited by Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (571 pages) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v13164 311 08$aPrint version: Nicosia, Giuseppe Machine Learning, Optimization, and Data Science Cham : Springer International Publishing AG,c2022 9783030954697 320 $aIncludes bibliographical references and index. 327 $aDeep Learning -- Machine Learning -- Reinforcement Learning -- Neural Networks -- Deep Reinforcement Learning -- Optimization -- Global Optimization -- Multi-Objective Optimization -- Computational Optimization -- Data Science -- Big Data -- Data Analytics -- Artificial Intelligence. 330 $aThis two-volume set, LNCS 13163-13164, constitutes the refereed proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, together with the first edition of the Symposium on Artificial Intelligence and Neuroscience, ACAIN 2021. The total of 86 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 215 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI,$x2946-1642 ;$v13164 606 $aArtificial intelligence 606 $aAlgorithms 606 $aApplication software 606 $aNumerical analysis 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aArtificial Intelligence 606 $aDesign and Analysis of Algorithms 606 $aComputer and Information Systems Applications 606 $aNumerical Analysis 606 $aComputer Communication Networks 606 $aComputer Application in Social and Behavioral Sciences 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aApplication software. 615 0$aNumerical analysis. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 14$aArtificial Intelligence. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputer and Information Systems Applications. 615 24$aNumerical Analysis. 615 24$aComputer Communication Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a005.7 676 $a006.31 702 $aNicosia$b Giuseppe 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522560303321 996 $aMachine learning, optimization, and data science$91949690 997 $aUNINA