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020 _a9783032067470
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024 7 _a10.1007/978-3-032-06747-0
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100 1 _aKhan, Noor Muhammad.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_923871
245 1 0 _aClassical and Bayesian Statistical Approaches in Infectious Disease Data Analysis
_h[electronic resource] /
_cby Noor Muhammad Khan, Ileana Baldi, Maria Vittoria Chiaruttini, Dario Gregori.
250 _a1st ed. 2026.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2026.
300 _aXX, 336 p. 86 illus., 71 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
341 0 _bPDF/UA-1
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341 0 _bTable of contents navigation
_2onix
341 0 _bSingle logical reading order
_2onix
341 0 _bShort alternative textual descriptions
_2onix
341 0 _bUse of color is not sole means of conveying information
_2onix
341 0 _bUse of high contrast between text and background color
_2onix
341 0 _bNext / Previous structural navigation
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341 0 _bAll non-decorative content supports reading without sight
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347 _atext file
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505 0 _aChapter 1 Bayesian and Frequentist Approaches in Infectious Disease Data Analysis -- Chapter 2 Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Independent Data -- Chapter 3 Variable Selection in Generalized Linear Models -- Chapter 4 Machine Learning Models for Probabilistic Inference and Prediction -- Chapter 5 Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data -- Chapter 6 Residuals and Overdispersion in Generalized Linear Models -- Chapter 7 Interrupted Time Series Model in Infectious Disease Research and Surveillance -- Chapter 8 Generalized Linear Models with Missing Data.
506 0 _aOpen Access
520 _aThis open access book is a comprehensive guide that delves into the statistical methodologies used in public health and infectious disease surveillance. It contrasts the foundational principles and methodologies of both Bayesian and Frequentist statistical approaches, providing a detailed exploration of how these methods are applied to the analysis and interpretation of infectious disease data. The book offers practical guidance on the application of these methods in real-life studies, both for surveillance and research purposes. It highlights the strengths and limitations of each approach and showcases how they can be effectively utilized in various scenarios. A set of R instructions and data examples to reproduce the analyses are provided. Among the topics covered are: Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Independent Data Machine Learning Models for Probabilistic Inference and Prediction Generalized Linear Models in Infectious Disease Analysis and Surveillance: Methods for Correlated Data Residuals and Overdispersion in Generalized Linear Models Interrupted Time Series Model in Infectious Disease Research and Surveillance Generalized Linear Models with Missing Data This topic is of particular importance to the field at this time due to the increasing need for accurate analysis and interpretation of infectious disease data, which is crucial for effective decision-making and policy formulation. Classical and Bayesian Statistical Approaches in Infectious Disease Data Analysis is primarily intended for public health professionals in local, national or international agencies; researchers and academics; students; and veterinary and one-health specialists. These readers would find this book valuable for its in-depth analysis, practical guidance, and the critical insights it provides into the application of statistical methods in the ever-evolving field of infectious disease surveillance.
532 8 _aAccessibility summary: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.
532 8 _aNo reading system accessibility options actively disabled
532 8 _aPublisher contact for further accessibility information: accessibilitysupport@springernature.com
650 0 _aStatistics .
_9905
650 0 _aEpidemiology.
650 0 _aPublic health.
650 0 _aDiseases.
650 0 _aMathematical statistics.
650 1 4 _aStatistical Theory and Methods.
_99338
650 2 4 _aEpidemiology.
650 2 4 _aPublic Health.
650 2 4 _aDiseases.
650 2 4 _aBayesian Inference.
650 2 4 _aMathematical Statistics.
700 1 _aBaldi, Ileana.
_eauthor.
_0(orcid)0000-0002-8578-9164
_1https://orcid.org/0000-0002-8578-9164
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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700 1 _aChiaruttini, Maria Vittoria.
_eauthor.
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_4http://id.loc.gov/vocabulary/relators/aut
_923873
700 1 _aGregori, Dario.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_923874
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783032067463
776 0 8 _iPrinted edition:
_z9783032067487
776 0 8 _iPrinted edition:
_z9783032067494
856 4 0 _uhttps://doi.org/10.1007/978-3-032-06747-0
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