Mathematical tools for understanding infectious diseases dynamics [[electronic resource] /] / Odo Diekmann, Hans Heesterbeek, and Tom Britton

• Autore: Diekmann O
• Pubblicazione: Princeton, : Princeton University Press, 2012
• Edizione: Course Book
• Descrizione fisica: 1 online resource (517 p.)
• Disciplina: 614.4
• Soggetto topico: Epidemiology - Mathematical models; Communicable diseases - Mathematical models
• Soggetto non controllato: Bayesian statistical inference; ICU model; Markov chain Monte Carlo method; Markov chain Monte Carlo methods; ReedІrost epidemic; age structure; asymptotic speed; bacterial infections; biological interpretation; closed population; compartmental epidemic systems; consistency conditions; contact duration; demography; dependence; disease control; disease outbreaks; disease prevention; disease transmission; endemic; epidemic models; epidemic outbreak; epidemic; epidemiological models; epidemiological parameters; epidemiology; general epidemic; growth rate; homogeneous community; hospital infections; hospital patients; host population growth; host; human social behavior; i-states; individual states; infected host; infection transmission; infection; infectious disease epidemiology; infectious disease; infectious diseases; infectious output; infective agent; infectivity; intensive care units; intrinsic growth rate; larvae; macroparasites; mathematical modeling; mathematical reasoning; maximum likelihood estimation; microparasites; model construction; outbreak situations; outbreak; pair approximation; parasite load; parasite; population models; propagation speed; reproduction number; separable mixing; sexual activity; stochastic epidemic model; structured population models; susceptibility; vaccination
• Classificazione: SCI008000MAT003000MED022090
• Altri autori: HeesterbeekHans <1960-> BrittonTom
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front matter -- Contents -- Preface -- Part I. The bare bones: Basic issues in the simplest context -- Part II. Structured populations -- Part III. Case studies on inference -- Part IV. Elaborations -- Bibliography -- Index
• Sommario/riassunto: 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
• Titolo autorizzato: Mathematical tools for understanding infectious diseases dynamics
• ISBN: 1-283-57875-1; 9786613891204; 1-4008-4562-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910785785403321
• Lo trovi qui: Univ. Federico II
• Serie: Princeton Series in Theoretical and Computational Biology