Deterministic and stochastic models of AIDS epidemics and HIV infections with intervention

Deterministic and stochastic models of AIDS epidemics and HIV infections with intervention

Detalles Bibliográficos
Formato: Libro electrónico
Idioma:Inglés
Publicado: Singapore : World Scientific 2005.
Colección:EBSCO Academic eBook Collection Complete.
Acceso en línea:Conectar con la versión electrónica
Ver en Universidad de Navarra:https://innopac.unav.es/record=b31816484*spi
Tabla de Contenidos:
  • Cover
  • Contents
  • Chapter 1 Mathematical Models for HIV Transmission Among Injecting Drug Users Vincenzo Capasso and Daniela Morale
  • 1. Introduction
  • 2. One Population Model
  • 2.1. Force of infection and social rules
  • 2.2. Qualitative analysis
  • 3. Multipopulation Models
  • 3.1. The force of infection
  • 3.2. Interaction among different populations
  • 3.3. Qualitative analysis
  • 3.4. About the uniqueness of the positive endemic equilibrium
  • 4. The Multistage Model
  • 5. Multipopulation models with multiple stages of infection
  • References
  • Chapter 2 Estimation of HIV Infection and Seroconversion Probabilities in IDU and Non-IDU Populations by State Space Models Wai-Yuan Tan, Li-Jun Zhang and Lih-Yuan Deng
  • 1. Introduction
  • 2. A Stochastic Model for HIV Infection and HIV Seroconversion
  • 2.1. Stochastic equations for the state variables
  • 2.2. The expected numbers of the state variables
  • 2.3. The probability distribution of the state variables
  • 3. Statistical Models and Data for HIV Seroconversion
  • 3.1. The time to event models for HIV seroconversion
  • 3.2. The data and a statistic model for seroconversion
  • 3.3. Statistical inferences on HIV seroconversion
  • 3.4. The Bayesian approach for estimating seroconversion
  • 3.5. A likelihood ratio test for comparing several HIV seroconversion distributions
  • 3.6. Estimation of HIV infection
  • 4. A State Space Model for HIV Seroconversion
  • 4.1. The stochastic system model and the probability distribution of state variables
  • 4.2. The observation model and the probability distribution of the number of the observed seroconvertors
  • 4.3. The contribution to the observed number of seroconverters by the data
  • 4.4. The conditional posterior distribution
  • 5. Simultaneous Estimation of Unknown Parameters and State Variables
  • 6. An Illustrative Example
  • 7. Conclusions and Discussion
  • Acknowledgements
  • References
  • Chapter 3 A Bayesian Monte Carlo Integration Strategy for Connecting Stochastic Models of HIV / AIDS with Data Charles J. Mode
  • 1. Introduction
  • 2. Basic Bayesian Concepts
  • 3. A Monte Carlo Integration Strategy
  • 4. On the Conditional Likelihood Function of the Data Given a Point in the Parameter Space and a Realization of the Process
  • 5. A Weighted Boot Strap Method for Resampling the Posterior Distribution
  • 6. Resampling the Posterior Distribution Based on the Largest Probabilities
  • 7. A Criterion for Selecting Sample Size
  • 8. Strategies for Confronting Issues of Computer Performance
  • 9. On Choosing Prior Distributions of the Parameters
  • References
  • Chapter 4 A Class of Methods for HIV Contact Tracing in Cuba: Implications for Intervention and Treatment Ying-Hen Hsieh, Hector de Arazoza, Rachid Lounes and Jose Joanes
  • 1. Introduction
  • 2. The Models
  • 2.1. The k2X model
  • 2.2. The k2Y model
  • 2.3. The k2XY model
  • 2.4. The k2XY/(X + Y) model
  • 3. Fitting the Models to Cuban Data
  • 4. Discussion and Concluding Remarks
  • Acknowledgments
  • References
  • Chapter 5 Simultaneous Inferences of HIV Vaccine Effects on Viral Load, CD4 Cell Counts, and Antiretroviral Therapy Initiation in Phase 3 Trials Peter B. Gilbert and Yanqing Sun
  • 1. Introduction
  • 2. Simultaneous Inferences for V E(, Xvl) and V E(, Xcd4)
  • 2.1. Notation and vaccine efficacy parameters of interest
  • 2.2. Point and simultaneous confidence interval estimates for vaccine efficacy
  • 2.3. Hypoth.

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