Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model | Sciact - Система учета научной деятельности ИМ СО РАН

Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model Научная публикация

Журнал

Journal of Applied and Industrial Mathematics
ISSN: 1990-4789 , E-ISSN: 1990-4797

Вых. Данные

Год: 2023, Том: 17, Номер: 1, Страницы: 94-109 Страниц : 15 DOI: 10.1134/s1990478923010118

Ключевые слова

agent-based model, COVID-19, inverse problem, optimization, regularization, scenario, basic reproduction number

Авторы

Krivorotko O.I. ^1,2,3 , Kabanikhin S.I. ^2,3 , Bektemesov M.A. ⁴ , Sosnovskaya M.I. ³ , Neverov A.V. ^1,3

Организации

1	Institute of Computational Mathematics and Mathematical Geophysics
2	Sobolev Institute of Mathematics
3	Novosibirsk State University
4	Abai Kazakh National Pedagogical University

Информация о финансировании (2)

1	Российский фонд фундаментальных исследований	21-51-10003
2	Президент РФ	МК-4994.2021.1.1

We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19 in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiological data and solving the inverse problem of reconstructing the parameters of the agent-based model (ABM) using the set of available epidemiological data. The main tool for constructing the ABM is the Covasim open library. In the event of a drastic change in the situation (appearance of a new strain, removal or introduction of restrictive measures, etc.), the model parameters are updated taking into account additional information for the previous month (online data assimilation). The inverse problem is solved by stochastic global optimization (of tree-structured Parzen estimators). As an example, we give two scenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January 20, 2022. The scenario that took into account the New Year holidays (published on December 12, 2021 on http://covid19-modeling.ru ) almost coincided with what happened in reality (the error was 0.2%)

Krivorotko O.I. , Kabanikhin S.I. , Bektemesov M.A. , Sosnovskaya M.I. , Neverov A.V.
Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model
Journal of Applied and Industrial Mathematics. 2023. V.17. N1. P.94-109. DOI: 10.1134/s1990478923010118 Scopus РИНЦ OpenAlex

Криворотько О.И. , Кабанихин С.И. , Бектемесов М.А. , Сосновская М.И. , Неверов А.В.
Моделирование сценариев распространения COVID-19 в Республике Казахстан на основе регуляризации агентной модели
Дискретный анализ и исследование операций. 2023. Т.30. №1. С.40-66. DOI: 10.33048/daio.2023.30.746 РИНЦ

Поступила в редакцию:	4 июл. 2022 г.
Принята к публикации:	28 сент. 2022 г.
Опубликована в печати:	16 мар. 2023 г.
Опубликована online:	15 мая 2023 г.

Scopus:	2-s2.0-85159854202
РИНЦ:	61421211
OpenAlex:	W4376621425

БД	Цитирований
Scopus	4
OpenAlex	4
РИНЦ	3