Modeling Epidemics: Neural Network Based on Data and SIR-Model Научная публикация
| Журнал |
Computational Mathematics and Mathematical Physics
ISSN: 0965-5425 , E-ISSN: 1555-6662 |
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| Вых. Данные | Год: 2023, Том: 63, Номер: 10, Страницы: 1929-1941 Страниц : 13 DOI: 10.1134/s096554252310007x | ||
| Ключевые слова | epidemiology, time series, machine learning, deep learning, data processing, recurrent neural networks, fully connected neural networks, COVID-19, prediction, forcasting | ||
| Авторы |
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| Организации |
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Информация о финансировании (1)
| 1 |
Министерство науки и высшего образования РФ Математический центр в Академгородке |
075-15-2019-1613, 075-15-2022-281 |
Реферат:
Earlier, a method for constructing an initial approximation for solving the inverse problem of acoustics by a gradient method based on a convolutional neural network trained to predict the distribution of velocities in a medium from wave response was proposed [9]. It was shown that the neural network trained on responses from simple layered media can be successfully used for solving the inverse problem for a significantly more complex model. In this paper, we present algorithms for processing data about epidemics and an example of applying a neural network for modeling the propagation of COVID-19 in Novosibirsk region (Russia) based only on data. A neural network NN-COVID-19 that uses data about the epidemics is constructed. It is shown that this neural network predicts the propagation of COVID-19 for five days by an order of magnitude better than SEIR-HCD. When a new variant (Omicron) appeared, this neural network was able to predict (after retraining) the propagation of the epidemics more accurately. Note that the proposed neural network uses not only epidemiological data but also social ones (such as holidays, restrictive measures, etc.). The proposed approach makes it possible to refine mathematical models. A comparison of the curves constructed by SEIRHCD model and by the neural network shows that the plots of solutions of the direct problem almost coincide with the plots constructed by the neural network. This helps refine coefficients of the differential model.
Библиографическая ссылка:
Krivorotko O.I.
, Zyatkov N.Y.
, Kabanikhin S.I.
Modeling Epidemics: Neural Network Based on Data and SIR-Model
Computational Mathematics and Mathematical Physics. 2023. V.63. N10. P.1929-1941. DOI: 10.1134/s096554252310007x WOS Scopus РИНЦ OpenAlex
Modeling Epidemics: Neural Network Based on Data and SIR-Model
Computational Mathematics and Mathematical Physics. 2023. V.63. N10. P.1929-1941. DOI: 10.1134/s096554252310007x WOS Scopus РИНЦ OpenAlex
Оригинальная:
Криворотько О.И.
, Зятьков Н.Ю.
, Кабанихин С.И.
Моделирование эпидемий: нейросеть на основе данных и SIR-модели
Журнал вычислительной математики и математической физики. 2023. Т.63. №10. С.1733-1746. DOI: 10.31857/S0044466923100095 РИНЦ OpenAlex
Моделирование эпидемий: нейросеть на основе данных и SIR-модели
Журнал вычислительной математики и математической физики. 2023. Т.63. №10. С.1733-1746. DOI: 10.31857/S0044466923100095 РИНЦ OpenAlex
Даты:
| Поступила в редакцию: | 22 мая 2023 г. |
| Принята к публикации: | 26 июн. 2023 г. |
| Опубликована в печати: | 20 нояб. 2023 г. |
| Опубликована online: | 20 нояб. 2023 г. |
Идентификаторы БД:
| Web of science: | WOS:001104729700015 |
| Scopus: | 2-s2.0-85176924309 |
| РИНЦ: | 64074182 |
| OpenAlex: | W4388718768 |