COMPUTER SIMULATION MODELS – DIAGNOSIS, TREATMENT AND CONTROL MEASURES FOR TUBERCULOSIS

Abstract

Tuberculosis, a bacterial disease caused by the intracellular pathogen. Mycobacterium tuberculosis and it is accounted for two to three million deaths per year. Causative agents are mostly localized in lymph nodes and are not easily controlled. The basic purpose of this review was to assess the diagnostic accuracy of computer simulation modeling as a screening tool for implementation in health care sectors. Therapeutics have limited scope for the treatment of Tuberculosis because they are composed of multiple drugs that retard the essential cellular pathways in Mycobacterium tuberculosis. Modeling Tools are effectual and widely used for designing control strategies. Onset of disease, epidemiological conditions, prophylactic, and genesis of disease is analyzed by simulation modeling. CAD software is used in lung segmentation. Meta-Disc is used for the exploration of heterogeneity. Artificial neural network is a three-layered back progression network having Matlab 7.0 version and it works like a brain for the detection of smear-negative samples. Constraint-based modeling provides a narrative approach to examine microbial metabolism but has not yet been applied to genome-scale modeling of Mycobacterium tuberculosis. Active case finding simulation tools are remarkably used to minimize the case detection-gap.