Forecasting trends in the tuberculosis epidemic situation using a simulation dynamic model

Abstract

Introduction Sverdlovsk region still has a high prevalence of HIV infection and tuberculosis, which affects the effectiveness of anti-tuberculosis measures. The new coronavirus infection for a number of years makes it necessary to redistribute the limited health care resources of the subject of the Russian Federation. This determines the need to develop science-based methods for analyzing and forecasting the development of the epidemic process in tuberculosis infection in the region.The aim of the work is to scientifically substantiate the method of forecasting the epidemic situation of tuberculosis on the territory of the subject with a set of municipalities with the construction of mathematical models and application of artificial intelligence.Materials and methods The source material for the study was statistical data obtained in 2007–2012 from state statistical reporting forms: Form No. 8 “Information about diseases with active tuberculosis”, Form No. 33 “Information about tuberculosis patients”, Forms 089u/tub, data from the Federal Register of Tuberculosis Patients, police registers of tuberculosis patients in health care institutions of Sverdlovsk region. Information processing was carried out using MS Excel, complex analytical tables of absolute values and epidemiological coefficients were compiled. Using artificial intelligence technology, a mathematical simulation dynamic model of the tuberculosis epidemic situation at the regional level and in the context of 63 municipalities of the Sverdlovsk region was developed.Results Comparison of the forecast values made in 2017 with the actual values of 2018–2021 revealed a reliable coincidence of the trend of movement of tuberculosis epidemiological indicators in the region, the maximum deviation was no more than 14.8 %.Discussion The proposed dynamic model made it possible to identify, reliably calculate and graphically display trends in the movement of the values of the studied characteristics of the tuberculosis epidemic process, despite the insignificant discrepancy between actual and forecast values.Conclusion The forecast results obtained using the simulation dynamic model can be used in practice for operational resource planning of resources for the implementation of measures to counter the spread of tuberculosis at the regional level.