MATHEMATICAL MODELING OF TRANSMISSION CO-INFECTION TUBERCULOSIS IN HIV COMMUNITY

• Main Author: Lusiana, Vina
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/44633
• Institution: Institut Teknologi Bandung

Tuberculosis (TB) and Human Immunodeciency Virus/Acquired Immuno- deciency Syndrome (HIV/AIDS) are major burden of infectious diseases in developing countries. TB and HIV infection have the eect of deeply on as- sault the immune system, since they can aord to weaken host immune respone through a mechanism that has not been fully understood. HIV coinfection is the most powerful known risk factor for progression of microbacterium tu- berculosis infection to active disease, as well as TB has been reported to ex- acerbate HIV infection. The number of people dying from HIV- associated to TB has been falling since 2003. However, there were still 320,000 deaths from HIV- associated to TB in 2012. In this thesis proposed to create a mo- del of transmission co-infection TB in HIV community, dynamic system with ten compartments built in here . Dynamic analysis in this paper mentioned ranging from disease free equilibrium conditions, endemic equilibrium condi- tions, basic reproduction ratio, stability analysis and numerical simulation. Basic reproductive ratio were obtained from spectral radius the next genera- tion matrix of the model. Numerical simulations are built to justify the results of the analysis and to see the changes in the dynamics of the population in each compartment. The sensitivity analysis indicates that the parameters af- fecting the population dynamics of co-infection TB in HIV infection individual is parameters rate of progression of individuals from the latent TB class to the active TB, treatment rate of latent TB individuals, treatment rate of infec- tious (active TB) individuals and probability of transmission of TB infection. Can conclude that growing number of infections carried by infectious TB in HIV infection individuals can lead to increased spread of disease or increase in endemic conditions.