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Deterministic And Stochastic Models Of Aids Epidemics And Hiv Infections With Intervention.
With contributions from an international team of leading researchers, the book pulls together updated research results in the area of HIV/AIDS modeling to provide readers with the latest information in the field. Topics covered include: AIDS epidemic models; vaccine models; models for HIV/cell dynam...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
River Edge :
World Scientific Publishing Company,
2006.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 Deng1. 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 seroconversion3.4. 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 Variables6. 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 Probabilities7. 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.