By Tan Wai-Yuan, Hulin Wu
With contributions from a world crew of prime researchers, the e-book pulls jointly up to date learn ends up in the realm of HIV/AIDS modeling to supply readers with the most recent details within the box. subject matters lined comprise: AIDS epidemic types; vaccine types; types for HIV/cell dynamics and interactions; mobile kinetics; viral dynamics with antiviral remedies; modeling of drug resistance and quasispecies. large deterministic versions, statistical versions, stochastic types and kingdom area types on treating AIDS sufferers with anti-retroviral medicines are supplied, in addition to an in-depth dialogue of those types. The booklet additionally comprises up-to-date studies on mathematical types for assessing results of AIDS vaccines, statistical tools for examining medical trial information on AIDS vaccines, and overviews of versions and statistical equipment for assessing drug resistance of HIV to anti- retroviral medications. a few vital statistical tools particular to the intervention and prevention of HIV epidemic also are mentioned. this may be an invaluable reference resource for graduate scholars and researchers in biomathematics and biostatistics, in addition to for HIV/AIDS epidemiologists and scientific investigators studying quantitative how you can examine AIDS epidemics and HIV an infection.
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Additional info for Deterministic And Stochastic Models Of Aids Epidemics And Hiv Infections With Intervention
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Under the above specification we have applied the methods in Sections 3 and 4 to the NIDA/NIH data to estimate the unknown parameters and the numbers of HIV infected people and HIV seroconverters monthly. 264. The probability densities of times to HIV seroconversion in populations of homosexuals, IDU and homosexuals with IDU are plotted in Fig. 1. Plotted in Fig. 2 are the probability densities of times to HIV infection and times to seroconversion in various populations respectively. Given in Table 1 are the means and variances of the HIV infection and seroconversion densities in various populations.
The transitions are from S to I and from I to C (that is, S → I → C). Let X (t) = {S(t), I(u, t), u = 0, 1, . . , t, C(t)} denote the numbers of S ∼ people, I(u) people and new seroconverters at the tth month respectively. Then we are entertaining a high dimensional stochastic process. This is basically a Markov chain with discrete time {t = 0, 1, . . , ∞}. Because the dimension of states increases with time t, the traditional approach is very complicated to be of much use. In this paper we will thus propose an alternative equivalent approach through stochastic difference equations.
This procedure for estimating Ω May 16, 2005 10:43WSPC/SPI-B259: Deterministic and Stochastic Models of AIDS and HIV with Intervention ch02 Estimation of HIV Infection 51 and the state variables given Y is described in Tan (2000, Chapter 6; 2002, Chapter 9) and is given by the following loops: (1) Combining a large sample from P {U , X|Θ} with the densities of yj in (30) through the weighted Bootstrap method due to Smith and Gelfand (1992), we generate {U , X} ( denote the generated sample {U (∗) , X (∗) }) from P {U , X|Θ, Y } although the latter density is unknown.