RESUMO
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.
RESUMO
This study proposes a modification of the GeoCity model previously developed by the authors, detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents. This made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of three types of strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. Calculations based on the proposed model show that SARS-CoV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the recipients, not the initiators of the epidemic. The simulations showed that fluctuations in the dynamics of various indicators of the spread of SARS-CoV 2 were associated with the difference in the daily schedule on weekdays and weekends. The results of the calculations showed that the daily schedules of people strongly influence the spread of SARS-CoV 2. Under assumptions of the model, the results show that for the more contagious "rapid" strains of SARS-CoV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS-CoV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious, or "fast" strain of the SARS-CoV 2 virus (omicron), spreads faster in public transport. For less contagious, or "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.
