Artificial intelligence for COVID-19 spread modeling Научная публикация
| Журнал |
Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945 |
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| Вых. Данные | Год: 2024, Том: 32, Номер: 2, Страницы: 297-332 Страниц : 36 DOI: 10.1515/jiip-2024-0013 | ||
| Ключевые слова | Data analysis; inverse problems; SIR models; agent-based models; optimization; forecasting; regularization; identifiability; nature inspired algorithms; machine learning | ||
| Авторы |
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| Организации |
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Информация о финансировании (2)
| 1 |
Министерство науки и высшего образования РФ Математический центр в Академгородке |
075-15-2019-1613, 075-15-2022-281 |
| 2 | Институт математики им. С.Л. Соболева СО РАН | FWNF-2024-0001 |
Реферат:
This paper presents classification and analysis of the mathematical models of the spread of COVID-19 in different groups of population such as family, school, office (3–100 people), town (100–5000 people), city, region (0.5–15 million people), country, continent, and the world. The classification covers major types of models (time-series, differential, imitation ones, neural networks models and their combinations). The time-series models are based on analysis of time series using filtration, regression and network methods. The differential models are those derived from systems of ordinary and stochastic differential equations as well as partial differential equations. The imitation models include cellular automata and agent-based models. The fourth group in the classification consists of combinations of nonlinear Markov chains and optimal control theory, derived by methods of the mean-field game theory. COVID-19 is a novel and complicated disease, and the parameters of most models are, as a rule, unknown and estimated by solving inverse problems. The paper contains an analysis of major algorithms of solving inverse problems: stochastic optimization, nature-inspired algorithms (genetic, differential evolution, particle swarm, etc.), assimilation methods, big-data analysis, and machine learning.
Библиографическая ссылка:
Krivorotko O.
, Kabanikhin S.I.
Artificial intelligence for COVID-19 spread modeling
Journal of Inverse and Ill-Posed Problems. 2024. V.32. N2. P.297-332. DOI: 10.1515/jiip-2024-0013 WOS Scopus РИНЦ OpenAlex
Artificial intelligence for COVID-19 spread modeling
Journal of Inverse and Ill-Posed Problems. 2024. V.32. N2. P.297-332. DOI: 10.1515/jiip-2024-0013 WOS Scopus РИНЦ OpenAlex
Даты:
| Поступила в редакцию: | 17 февр. 2024 г. |
| Принята к публикации: | 18 февр. 2024 г. |
| Опубликована online: | 20 мар. 2024 г. |
| Опубликована в печати: | 1 апр. 2024 г. |
Идентификаторы БД:
| Web of science: | WOS:001187133200001 |
| Scopus: | 2-s2.0-85189671878 |
| РИНЦ: | 66642400 |
| OpenAlex: | W4392951458 |