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Stochastic Epidemic Models with Inference / Edited by Tom Britton, and Etienne Pardoux

Contributor(s): Material type: TextTextLanguage: English Series: Mathematical Biosciences SubseriesPublication details: Cham : Springer, 2019.Description: xviii, 472 p. ; 24 cm. PBISBN:
  • 9783030308995
Subject(s): DDC classification:
  • 511.8 BRI
Contents:
- Part I Stochastic Epidemics in a Homogeneous Community. - Introduction. - Stochastic Epidemic Models. - Markov Models. - General Closed Models. - Open Markov Models. - Part II Stochastic SIR Epidemics in Structured Populations. - Introduction. - Single Population Epidemics. - The Households Model. - A General Two-Level Mixing Model. - Part III Stochastic Epidemics in a Heterogeneous Community. - Introduction. - Random Graphs. - The Reproduction Number R0. - SIR Epidemics on Configuration Model Graphs. - Statistical Description of Epidemics Spreading on Networks: The Case of Cuban HIV. - Part IV Statistical Inference for Epidemic Processes in a Homogeneous Community. - Introduction. - Observations and Asymptotic Frameworks. - Inference for Markov Chain Epidemic Models. - Inference Based on the Diffusion Approximation of Epidemic Models.- Inference for Continuous Time SIR models.
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Book Book Ranganathan Library Mathematics and Statistics 511.8 BRI (Browse shelf(Opens below)) Available 050081

- Part I Stochastic Epidemics in a Homogeneous Community. - Introduction. - Stochastic Epidemic Models. - Markov Models. - General Closed Models. - Open Markov Models. - Part II Stochastic SIR Epidemics in Structured Populations. - Introduction. - Single Population Epidemics. - The Households Model. - A General Two-Level Mixing Model. - Part III Stochastic Epidemics in a Heterogeneous Community. - Introduction. - Random Graphs. - The Reproduction Number R0. - SIR Epidemics on Configuration Model Graphs. - Statistical Description of Epidemics Spreading on Networks: The Case of Cuban HIV. - Part IV Statistical Inference for Epidemic Processes in a Homogeneous Community. - Introduction. - Observations and Asymptotic Frameworks. - Inference for Markov Chain Epidemic Models. - Inference Based on the Diffusion Approximation of Epidemic Models.- Inference for Continuous Time SIR models.

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