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

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Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model. / Krivorotko, O. I.; Kabanikhin, S. I.; Bektemesov, M. A. et al.

In: Journal of Applied and Industrial Mathematics, Vol. 17, No. 1, 03.2023, p. 94-109.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{474123bf645d48008de3a2776a1096ed,

title = "Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model",

abstract = "Abstract: We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiologicaldata 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 isthe Covasim open library. In theevent of a drastic change in the situation (appearance of a new strain, removal or introduction ofrestrictive measures, etc.), the model parameters are updated taking into account additionalinformation for the previous month (online data assimilation). The inverse problem is solved bystochastic global optimization (of tree-structured Parzen estimators). As an example, we give twoscenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January20, 2022. The scenario that took into account the New Year holidays (published on December 12,2021 on http://covid19-modeling.ru) almost coincided withwhat happened in reality (the error was 0.2%).",

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

author = "Krivorotko, {O. I.} and Kabanikhin, {S. I.} and Bektemesov, {M. A.} and Sosnovskaya, {M. I.} and Neverov, {A. V.}",

note = "This research was supported by the Council for Grants under the President of the Russian Federation, project no. MK–4994.2021.1.1, the Russian Foundation for Basic Research, project no. 21–51–10003, and the Ministry of Education and Science of the Republic of Kazakhstan, project no. AP09260317. Публикация для корректировки.",

year = "2023",

month = mar,

doi = "10.1134/S1990478923010118",

language = "English",

volume = "17",

pages = "94--109",

journal = "Journal of Applied and Industrial Mathematics",

issn = "1990-4789",

publisher = "Maik Nauka-Interperiodica Publishing",

number = "1",

}

RIS

TY - JOUR

T1 - Simulation of COVID-19 Spread Scenarios in the Republic of Kazakhstan Based on Regularization of the Agent-Based Model

AU - Krivorotko, O. I.

AU - Kabanikhin, S. I.

AU - Bektemesov, M. A.

AU - Sosnovskaya, M. I.

AU - Neverov, A. V.

N1 - This research was supported by the Council for Grants under the President of the Russian Federation, project no. MK–4994.2021.1.1, the Russian Foundation for Basic Research, project no. 21–51–10003, and the Ministry of Education and Science of the Republic of Kazakhstan, project no. AP09260317. Публикация для корректировки.

PY - 2023/3

Y1 - 2023/3

N2 - Abstract: We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiologicaldata 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 isthe Covasim open library. In theevent of a drastic change in the situation (appearance of a new strain, removal or introduction ofrestrictive measures, etc.), the model parameters are updated taking into account additionalinformation for the previous month (online data assimilation). The inverse problem is solved bystochastic global optimization (of tree-structured Parzen estimators). As an example, we give twoscenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January20, 2022. The scenario that took into account the New Year holidays (published on December 12,2021 on http://covid19-modeling.ru) almost coincided withwhat happened in reality (the error was 0.2%).

AB - Abstract: We propose an algorithm for modeling scenarios for newly diagnosed cases of COVID-19in the Republic of Kazakhstan. The algorithm is based on treating incomplete epidemiologicaldata 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 isthe Covasim open library. In theevent of a drastic change in the situation (appearance of a new strain, removal or introduction ofrestrictive measures, etc.), the model parameters are updated taking into account additionalinformation for the previous month (online data assimilation). The inverse problem is solved bystochastic global optimization (of tree-structured Parzen estimators). As an example, we give twoscenarios of COVID-19 propagation calculated on December 12, 2021 for the period up to January20, 2022. The scenario that took into account the New Year holidays (published on December 12,2021 on http://covid19-modeling.ru) almost coincided withwhat happened in reality (the error was 0.2%).

KW - COVID-19

KW - agent-based model

KW - basic reproduction number

KW - inverse problem

KW - optimization

KW - regularization

KW - scenario

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85159854202&origin=inward&txGid=8c3bd2d7552417ec15f8d68e61afaccb

UR - https://www.mendeley.com/catalogue/dd69ad10-8d04-3f4d-92d4-65aa10fe6e97/

U2 - 10.1134/S1990478923010118

DO - 10.1134/S1990478923010118

M3 - Article

VL - 17

SP - 94

EP - 109

JO - Journal of Applied and Industrial Mathematics

JF - Journal of Applied and Industrial Mathematics

SN - 1990-4789

IS - 1

ER -

ID: 59243426