Forecasting Trends in the Tuberculosis Epidemic Situation in the Region of the Russian Federation by Dynamic Simulation Model
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Forecasting Trends in the Tuberculosis Epidemic Situation in the Region of the Russian Federation by Dynamic Simulation Model
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| Автор |
Cherniaev, I. A.
Chernavin, P. F. Tsvetkov, A. I. Chugaev, Y. P. Cherniaeva, U. I. Chernavin, N. P. |
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| Тематика |
DYNAMIC SIMULATION MODEL
EPIDEMIOLOGY OF TUBERCULOSIS MACHINE LEARNING ARTIFICIAL INTELLIGENCE EPIDEMICS FORECASTING HIV INFECTIONS HUMANS RUSSIA TUBERCULOSIS BIOTECHNOLOGY CORONAVIRUS DISEASES FORECASTING MACHINE LEARNING TUBES (COMPONENTS) ARTIFICIAL INTELLIGENCE METHODS COMMERCIAL LICENSE CORONAVIRUSES CREATIVE COMMONS DYNAMIC SIMULATION MODELS EPIDEMIOLOGY OF TUBERCULOSIS HIV INFECTION MACHINE-LEARNING OPENACCESS RUSSIAN FEDERATION ARTIFICIAL INTELLIGENCE EPIDEMIC FORECASTING HUMAN HUMAN IMMUNODEFICIENCY VIRUS INFECTION RUSSIAN FEDERATION TUBERCULOSIS EPIDEMIOLOGY |
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| Описание |
The spread of a new coronavirus infection in the last two years together with HIV infection preserves and even increases the potential for the spread of tuberculosis in the world. Sverdlovsk oblast (SO) of Russian Federation is the region with high levels of HIV and tuberculosis (TB). The search for new methods of forecasting of the future epidemic situation for tuberculosis has become particularly relevant. The aim was to develop an effective method for predicting the epidemic situation of tuberculosis using an artificial intelligence (AI) method in the format of a dynamic simulation model based on AI technologies. Statistical data was loaded from the state statistical reporting on TB patients for the period 2007-2017. The parameters were controlled through a system of inequalities. The proposed SDM made it possible to identify and reliably calculate trends of TB epidemiological indicators. Comparison of the predicted values made in 2017 with the actual values of 2018-2021 revealed a reliable coincidence of the trend of movement of TB epidemiological indicators in the region, the maximum deviation was no more than 14.82%. The forecast results obtained with SDM are quite suitable for practical use. Especially, in operational resource planning of measures to counteract the spread of tuberculosis at the regional level. © 2022 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
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| Дата |
2024-04-08T11:07:39Z
2024-04-08T11:07:39Z 2022 |
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| Тип |
Conference paper
Conference object (info:eu-repo/semantics/conferenceObject) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Cherniaev, IA, Chernavin, PF, Tsvetkov, AI, Chugaev, YP, Cherniaeva, UI & Chernavin, NP 2022, Forecasting Trends in the Tuberculosis Epidemic Situation in the Region of the Russian Federation by Dynamic Simulation Model. в B Blobel, B Yang & M Giacomini (ред.), pHealth 2022 - Proceedings of the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health. Studies in Health Technology and Informatics, Том. 299, IOS Press BV, стр. 235-241, 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2022, Oslo, Норвегия, 08/11/2022. https://doi.org/10.3233/SHTI220990
Cherniaev, I. A., Chernavin, P. F., Tsvetkov, A. I., Chugaev, Y. P., Cherniaeva, U. I., & Chernavin, N. P. (2022). Forecasting Trends in the Tuberculosis Epidemic Situation in the Region of the Russian Federation by Dynamic Simulation Model. в B. Blobel, B. Yang, & M. Giacomini (Ред.), pHealth 2022 - Proceedings of the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health (стр. 235-241). (Studies in Health Technology and Informatics; Том 299). IOS Press BV. https://doi.org/10.3233/SHTI220990 978-164368348-5 0926-9630 Final All Open Access; Hybrid Gold Open Access https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI220990 https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI220990 http://elar.urfu.ru/handle/10995/131494 10.3233/SHTI220990 85141210897 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by-nc https://creativecommons.org/licenses/by-nc/4.0/ |
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| Формат |
application/pdf
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| Издатель |
IOS Press BV
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| Источник |
pHealth 2022
Studies in Health Technology and Informatics |
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