Fuzzy modeling in symptomatic HIV virus infected population

Abstract

This paper introduces a model for the evolution of positive HIV population and manifestation of AIDS (acquired immunideficiency syndrome). The focus is on the nature of the transference rate of HIV to AIDS. Expert knowledge indicates that the transference rate is uncertain and depends strongly on the viral load and the CD4+ level of the infected individuals. Here, we suggest to view the transference rate as a fuzzy set of the viral load and CD4+ level values. In this case the dynamic model results in a fuzzy model that preserves the biological meaning and nature of the transference rate lambda. Its behavior fits the natural history of HIV infection reported in the medical science domain. The paper also includes a comparison between the fuzzy model and a classic Anderson's model using data reported in the literature.

Studies on Population Dynamics Using Cellular Automata

“…Recently, several authors have advocated the use of fuzzy set theory to address epidemiology problems Jafelice et al, 2004;Ortega et al, 2003) and population dynamics (Krivan & Colombo, 1998). Since the advent of the HIV infection, several mathematical models have been developed to describe the HIV dynamics (Murray, 1990;Nowak & Bangham, 1996;Nowak, 1999).…”

“…The output processor task is to provide real-valued outputs using defuzzification, a process that chooses a real number that is representative of the fuzzy set inferred. A typical defuzzification scheme, the one adopted in this paper, is the centroid or center of mass method (Jafelice et al, 2004).…”

Existence of Solutions to Boundary Value Problems for a Class of Nonlinear Fuzzy Fractional Differential Equations

“…[2]). Nowadays, the theory of fuzzy differential equations could be applicable in many areas, for instance, physics, thermodynamics, biology, medicine, chemistry and many other fields of science (see e. g., [3,4,5,6,7] and references contained therein).…”

Analogia da Regra Composicional de Inferência e Operadores Lineares

“…Chamamos método de inferência a técnica usada para fazer conclusões usando um conjunto de regras nebulosas. Aplicações de conjuntos nebulosos inclue dinâmica populacional [1,9,10], diagnóstico [3], controle [4,13], otimização [5] e previsão de séries temporais [21,22]. E importante esclarecer, entretanto, que a teoria dos conjuntos nebulosos e a lógica nebulosa não são teorias nebulosas ou vagas [18,25].…”