Introduction: The paper presents epidemiological process modeling, with a focus on tuberculosis utilizing multi-agent system.
Material and methods: This study involves the development of an algorithm that harnesses the potential of artificial intelligence to create a geospatial model that highlights the different pathways of TB transmission. The modeling process itself is characterized by a series of key stages, including initialization of the city, calibration of health parameters, simulation of the working day, propagation of the spread of infection, the evolution of disease trajectories, rigorous statistical calculations and transition to the following day. A comprehensive description of the course of active tuberculosis is presented, following the official hypothesis recommended by the World Health Organization. A comprehensive simulation, illustrating the propagation of tuberculosis in an entirely healthy environment devoid of any preventive or therapeutic measures, is presented. To ascertain the adequacy of the model and its sensitivity to the principal parameters governing the course of tuberculosis, a series of experiments were meticulously conducted, employing three distinct approximations, namely: the basic model, the model incorporating mortality factors, and the comprehensive model, encompassing all relevant aspects.
Conclusions: The model's results exhibit stability and lack of significant fluctuations. The statistical values obtained for infected, latent, and recovered individuals align well with known medical data, confirming the model's adequacy.
Keywords: Geo-object; GeoCity; Multi-agent modeling; Resident; Tuberculosis.
© 2024 The Authors.