Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model Full article
| Journal |
Journal of Applied and Industrial Mathematics
ISSN: 1990-4789 , E-ISSN: 1990-4797 |
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| Output data | Year: 2023, Volume: 17, Number: 1, Pages: 94-109 Pages count : 15 DOI: 10.1134/s1990478923010118 | ||||||||
| Tags | agent-based model, COVID-19, inverse problem, optimization, regularization, scenario, basic reproduction number | ||||||||
| Authors |
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| Affiliations |
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Funding (2)
| 1 | Russian Foundation for Basic Research | 21-51-10003 |
| 2 | Президент РФ | МК-4994.2021.1.1 |
Abstract:
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%)
Cite:
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
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
Original:
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
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
Dates:
| Submitted: | Jul 4, 2022 |
| Accepted: | Sep 28, 2022 |
| Published print: | Mar 16, 2023 |
| Published online: | May 15, 2023 |
Identifiers:
| Scopus: | 2-s2.0-85159854202 |
| Elibrary: | 61421211 |
| OpenAlex: | W4376621425 |