A Markov Chain Monte Carlo Approach to Estimate AIDS after HIV Infection. Apenteng OO, Ismail NA - PLoS ONE (2015) Bottom Line: The spread of human immunodeficiency virus (HIV) infection and the resulting acquired immune deficiency syndrome (AIDS) is a major health concern in many parts of the world, and mathematical models are commonly applied to understand the spread of the HIV epidemic.The current study used this framework to assess the interaction between individuals who developed AIDS after HIV infection and individuals who did not develop AIDS after HIV infection (pre-AIDS).Finally, to examine this framework and demonstrate how it works, a case study was performed of reported HIV and AIDS cases from an annual data set in Malaysia, and then we compared how these approaches complement each other. View Article: PubMed Central - PubMed Affiliation: Department of Applied Statistics, Faculty of Economics & Administration, University of Malaya, Kuala Lumpur, Malaysia. ABSTRACTThe spread of human immunodeficiency virus (HIV) infection and the resulting acquired immune deficiency syndrome (AIDS) is a major health concern in many parts of the world, and mathematical models are commonly applied to understand the spread of the HIV epidemic. To understand the spread of HIV and AIDS cases and their parameters in a given population, it is necessary to develop a theoretical framework that takes into account realistic factors. The current study used this framework to assess the interaction between individuals who developed AIDS after HIV infection and individuals who did not develop AIDS after HIV infection (pre-AIDS). We first investigated how probabilistic parameters affect the model in terms of the HIV and AIDS population over a period of time. We observed that there is a critical threshold parameter, R0, which determines the behavior of the model. If R0 ≤ 1, there is a unique disease-free equilibrium; if R0 < 1, the disease dies out; and if R0 > 1, the disease-free equilibrium is unstable. We also show how a Markov chain Monte Carlo (MCMC) approach could be used as a supplement to forecast the numbers of reported HIV and AIDS cases. An approach using a Monte Carlo analysis is illustrated to understand the impact of model-based predictions in light of uncertain parameters on the spread of HIV. Finally, to examine this framework and demonstrate how it works, a case study was performed of reported HIV and AIDS cases from an annual data set in Malaysia, and then we compared how these approaches complement each other. We conclude that HIV disease in Malaysia shows epidemic behavior, especially in the context of understanding and predicting emerging cases of HIV and AIDS. No MeSH data available. Related in: MedlinePlus © Copyright Policy Related In: Results  -  Collection License getmorefigures.php?uid=PMC4493079&req=5 .flowplayer { width: px; height: px; } pone.0131950.g006: Sensitivity range of yearly reported HIV and AIDS cases.The high variances were observed in the following compartment order: A1 > A2 > I > S. This shows that there was predictive accuracy of the model reflected by the variance of the predictive distribution. The large number for the variance is due to either the uncertainties in the model or noise in data collection, and the model fit the noisy data reasonably well. Mentions: As shown in Fig 6, the high variances were observed in the following compartment order: S > I > A2 > A1, which shows the predictive accuracy of the model reflected by the variance of the predictive distribution. The large variance is due to either the uncertainties in the model or noise in data collection, and this model fit the noisy data reasonably well.

A Markov Chain Monte Carlo Approach to Estimate AIDS after HIV Infection.

Apenteng OO, Ismail NA - PLoS ONE (2015)

Related In: Results  -  Collection

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pone.0131950.g006: Sensitivity range of yearly reported HIV and AIDS cases.The high variances were observed in the following compartment order: A1 > A2 > I > S. This shows that there was predictive accuracy of the model reflected by the variance of the predictive distribution. The large number for the variance is due to either the uncertainties in the model or noise in data collection, and the model fit the noisy data reasonably well.
Mentions: As shown in Fig 6, the high variances were observed in the following compartment order: S > I > A2 > A1, which shows the predictive accuracy of the model reflected by the variance of the predictive distribution. The large variance is due to either the uncertainties in the model or noise in data collection, and this model fit the noisy data reasonably well.

Bottom Line: The spread of human immunodeficiency virus (HIV) infection and the resulting acquired immune deficiency syndrome (AIDS) is a major health concern in many parts of the world, and mathematical models are commonly applied to understand the spread of the HIV epidemic.The current study used this framework to assess the interaction between individuals who developed AIDS after HIV infection and individuals who did not develop AIDS after HIV infection (pre-AIDS).Finally, to examine this framework and demonstrate how it works, a case study was performed of reported HIV and AIDS cases from an annual data set in Malaysia, and then we compared how these approaches complement each other.

View Article: PubMed Central - PubMed

Affiliation: Department of Applied Statistics, Faculty of Economics & Administration, University of Malaya, Kuala Lumpur, Malaysia.

ABSTRACT
The spread of human immunodeficiency virus (HIV) infection and the resulting acquired immune deficiency syndrome (AIDS) is a major health concern in many parts of the world, and mathematical models are commonly applied to understand the spread of the HIV epidemic. To understand the spread of HIV and AIDS cases and their parameters in a given population, it is necessary to develop a theoretical framework that takes into account realistic factors. The current study used this framework to assess the interaction between individuals who developed AIDS after HIV infection and individuals who did not develop AIDS after HIV infection (pre-AIDS). We first investigated how probabilistic parameters affect the model in terms of the HIV and AIDS population over a period of time. We observed that there is a critical threshold parameter, R0, which determines the behavior of the model. If R0 ≤ 1, there is a unique disease-free equilibrium; if R0 < 1, the disease dies out; and if R0 > 1, the disease-free equilibrium is unstable. We also show how a Markov chain Monte Carlo (MCMC) approach could be used as a supplement to forecast the numbers of reported HIV and AIDS cases. An approach using a Monte Carlo analysis is illustrated to understand the impact of model-based predictions in light of uncertain parameters on the spread of HIV. Finally, to examine this framework and demonstrate how it works, a case study was performed of reported HIV and AIDS cases from an annual data set in Malaysia, and then we compared how these approaches complement each other. We conclude that HIV disease in Malaysia shows epidemic behavior, especially in the context of understanding and predicting emerging cases of HIV and AIDS.

No MeSH data available.

Related in: MedlinePlus