4 resultados para HIV (Virus) - Modelos animais
em Instituto Politécnico do Porto, Portugal
Resumo:
In this paper we study a delay mathematical model for the dynamics of HIV in HIV-specific CD4 + T helper cells. We modify the model presented by Roy and Wodarz in 2012, where the HIV dynamics is studied, considering a single CD4 + T cell population. Non-specific helper cells are included as alternative target cell population, to account for macrophages and dendritic cells. In this paper, we include two types of delay: (1) a latent period between the time target cells are contacted by the virus particles and the time the virions enter the cells and; (2) virus production period for new virions to be produced within and released from the infected cells. We compute the reproduction number of the model, R0, and the local stability of the disease free equilibrium and of the endemic equilibrium. We find that for values of R0<1, the model approaches asymptotically the disease free equilibrium. For values of R0>1, the model approximates asymptotically the endemic equilibrium. We observe numerically the phenomenon of backward bifurcation for values of R0⪅1. This statement will be proved in future work. We also vary the values of the latent period and the production period of infected cells and free virus. We conclude that increasing these values translates in a decrease of the reproduction number. Thus, a good strategy to control the HIV virus should focus on drugs to prolong the latent period and/or slow down the virus production. These results suggest that the model is mathematically and epidemiologically well-posed.
Resumo:
We study a mathematical model for the human immunodeficiency virus (HIV) and hepatites C virus (HCV) coinfection. The model predicts four distinct equilibria: the disease free, the HIV endemic, the HCV endemic, and the full endemic equilibria. The local and global stability of the disease free equilibrium was calculated for the full model and the HIV and HCV submodels. We present numerical simulations of the full model where the distinct equilibria can be observed. We show simulations of the qualitative changes of the dynamical behavior of the full model for variation of relevant parameters. From the results of the model, we infer possible measures that could be implemented in order to reduce the number of infected individuals.
Resumo:
A evolução digital proporcionou às sociedades uma facilidade extraordinária de comunicação. Com o número crescente de computadores e o aumento de acessos à internet, surgiu uma nova forma de criminologia, que cresceu em paralelo com o número de utilizadores. Desta forma, tornou-se comum a criação e difusão de vírus informáticos pelos chamados hackers. Neste trabalho estudam-se modelos de transmissão de vírus informáticos, usando modelos epidemiológicos. Começa-se por fazer uma revisão dos modelos existentes na literatura, de seguida sugerem-se alterações a esses modelos de forma a conseguir uma melhor aproximação à dinâmica real de transmissão de vírus informáticos. As simulações numéricas dos modelos permitem-nos inferir de que uma forma de controlar a transmissão de vírus informáticos é a diminuição da taxa de infeção, isto é, da taxa de transmissão do vírus. No último capítulo enumeram-se as conclusões do trabalho efetuado e indicam-se direções de trabalho futuro.
Resumo:
We develop a new a coinfection model for hepatitis C virus (HCV) and the human immunodeficiency virus (HIV). We consider treatment for both diseases, screening, unawareness and awareness of HIV infection, and the use of condoms. We study the local stability of the disease-free equilibria for the full model and for the two submodels (HCV only and HIV only submodels). We sketch bifurcation diagrams for different parameters, such as the probabilities that a contact will result in a HIV or an HCV infection. We present numerical simulations of the full model where the HIV, HCV and double endemic equilibria can be observed. We also show numerically the qualitative changes of the dynamical behavior of the full model for variation of relevant parameters. We extrapolate the results from the model for actual measures that could be implemented in order to reduce the number of infected individuals.