RESUMO
HIV interaction with the immune response is modeled mathematically. Initially, a detailed model is proposed that consists of a system of differential equations including immune cells (antigen presenting cells, T latent infected cells, T actively infected cells, resting T cells, helper T cells, inactive cytotoxic cells and active cytotoxic cells) and viral particles. Then, stability conditions are given from the basic reproduction number and numerical simulations are performed. From this it is possible to conclude what are the most influential parameters to reduce infection. From the initial model, a control problem is formulated in order to determine the most appropriate type of intervention to ensure high levels of activated T cells and immune response. Five different control strategies based on antiretroviral are evaluated to conclude that a strategy of constant control, obtained as the average value of optimal control, provides satisfactory results.
Se modela matemáticamente la interacción del VIH con la respuesta inmune. Inicialmente se construye un modelo de manera detallada, que consiste en un sistema de ecuaciones diferenciales que incluye células del sistema inmune (células presentadoras de antígenos, células T infectadas en estado de latencia, células T infectadas activadas, células T en reposo, células T colaboradoras, células de respuesta citotóxica inactivas y células de respuesta citotóxica activas) y partículas virales. A continuación se dan condiciones de estabilidad a partir del número básico de reproducción y se hacen simulaciones numéricas que permiten concluir cuáles son los parámetros más influyentes si se desea reducir la infección. A partir del modelo inicial, se formula un Problema de Control con el objetivo de determinar el tipo de intervención más apropiado que asegure niveles altos de células T activas y de respuesta inmune. Se evalúan entonces cinco estrategias de control diferentes basadas en antirretrovirales y se concluye que una estrategia de control constante, obtenida como el valor promedio del control óptimo, brinda resultados satisfactorios.
Assuntos
Fármacos Anti-HIV/uso terapêutico , Infecções por HIV/tratamento farmacológico , Infecções por HIV/imunologia , Modelos Imunológicos , Esquema de Medicação , HIV/imunologia , Infecções por HIV/virologia , Humanos , Resultado do TratamentoRESUMO
RESUMEN Se modela matemáticamente la interacción del VIH con la respuesta inmune. Inicialmente se construye un modelo de manera detallada, que consiste en un sistema de ecuaciones diferenciales que incluye células del sistema inmune (células presentadoras de antígenos, células T infectadas en estado de latencia, células T infectadas activadas, células T en reposo, células T colaboradoras, células de respuesta citotóxica inactivas y células de respuesta citotóxica activas) y partículas virales. A continuación se dan condiciones de estabilidad a partir del número básico de reproducción y se hacen simulaciones numéricas que permiten concluir cuáles son los parámetros más influyentes si se desea reducir la infección. A partir del modelo inicial, se formula un Problema de Control con el objetivo de determinar el tipo de intervención más apropiado que asegure niveles altos de células T activas y de respuesta inmune. Se evalúan entonces cinco estrategias de control diferentes basadas en antirretrovirales y se concluye que una estrategia de control constante, obtenida como el valor promedio del control óptimo, brinda resultados satisfactorios.(AU)
ABSTRACT HIV interaction with the immune response is modeled mathematically. Initially, a detailed model is proposed that consists of a system of differential equations including immune cells (antigen presenting cells, T latent infected cells, T actively infected cells, resting T cells, helper T cells, inactive cytotoxic cells and active cytotoxic cells) and viral particles. Then, stability conditions are given from the basic reproduction number and numerical simulations are performed. From this it is possible to conclude what are the most influential parameters to reduce infection. From the initial model, a control problem is formulated in order to determine the most appropriate type of intervention to ensure high levels of activated T cells and immune response. Five different control strategies based on antiretroviral are evaluated to conclude that a strategy of constant control, obtained as the average value of optimal control, provides satisfactory results.(AU)
Assuntos
Humanos , Infecções por HIV/tratamento farmacológico , Modelos Estatísticos , Terapia Combinada , Antirretrovirais/uso terapêutico , Sistema ImunitárioRESUMO
Nonlinear differential equations were used for formulating a mathematical model describing the dynamics of HIV interaction with CD4 T-cells which are considered to be activated or not activated during recognition of viral particles; in either case they are susceptible to HIV infection. The system's equilibrium points were found and local stability was determined for trivial equilibrium or the absence of infection based on the basic reproduction number. The model was used for numerical simulation to show infected cell and viral load patterns regarding the variations of some parameters. The model was then reformulated, considering a cytotoxic cellular immune response and numerical simulation was run again.
Assuntos
Linfócitos T CD4-Positivos/imunologia , Citotoxicidade Imunológica , Infecções por HIV/imunologia , Modelos Teóricos , HumanosRESUMO
Con base en ecuaciones diferenciales no lineales se formula un modelo matemático que describe la dinámica de interacción del VIH con células T CD4, que se considera que pueden ser activadas o no activadas en el reconocimiento de partículas virales; en cualquiera de los dos casos, son susceptibles a la infección con el virus. Se encuentran los puntos de equilibrio del sistema y en el caso particular del equilibrio trivial o de ausencia de infección, se determina su estabilidad local con base en el número básico de reproducción. Se efectúa además la simulación numérica del modelo para establecer el comportamiento que presentan las células infectadas y la carga viral, frente a variaciones de algunos de los parámetros. Finalmente, se reformula el modelo considerando respuesta inmune celular de tipo citotóxico y se realiza la simulación numérica.
Nonlinear differential equations were used for formulating a mathematical model describing the dynamics of HIV interaction with CD4 T-cells which are considered to be activated or not activated during recognition of viral particles; in either case they are susceptible to HIV infection. The system's equilibrium points were found and local stability was determined for trivial equilibrium or the absence of infection based on the basic reproduction number. The model was used for numerical simulation to show infected cell and viral load patterns regarding the variations of some parameters. The model was then reformulated, considering a cytotoxic cellular immune response and numerical simulation was run again.
Assuntos
Humanos , /imunologia , Citotoxicidade Imunológica , Infecções por HIV/imunologia , Modelos TeóricosRESUMO
A mathematical model was constructed for modelling transmission dynamics and the evolution of an infectious disease in a prison setting, considering asymptomatic infectious people, symptomatic infectious people and isolated infectious people. The model was proposed as a nonlinear differential equation system for describing disease epidemiology. The model's stability was analysed for including a preventative control strategy which would enable finding a suitable basic reproduction number-based control protocol. A cost function related to the system of differential equations was formulated to minimise infectious populations and intervention costs; such function was minimised by using the Pontryagin maximum principle which determines optimum preventative control strategies by minimising both infectious populations and associated costs. A numerical analysis of the model was made, considering preventative control effectiveness levels and different control weighting constants. Conclusions were drawn. The basic reproduction number characterises system stability and leads to determining clear control criteria; a preventative control threshold was defined, based on the controlled basic reproduction number which enabled deducing that disease control requires uniform preventative control involving high rates of effectiveness.
