Geodesic
Simulation of COVID-19 propagation scenarios in~the Republic of Kazakhstan based~on~regularization of agent model
Diskretnyj analiz i issledovanie operacij, Tome 30 (2023) no. 1, pp. 40-66

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An algorithm for modeling scenarios for new diagnosed cases of COVID-19 in the Republic of Kazakhstan is proposed. The algorithm is based on the treatment of incomplete epidemiological data and the inverse problem solving for the agent-based model (ABM) using a set of available epidemiological data. The main tool for building the ABM is the open library Covasim. In the event of a sudden change in the situation (appearance of a new strain, removal or introduction of restrictive measures, etc.), the model parameters are updated with additional information for the previous month (data assimilation). The inverse problem was solved by tree Parzen estimates optimization. As an example, two scenarios of COVID-19 propagation are given, calculated on December 12, 2021 for the period up to January 20, 2022. The scenario, which took into account the New Year holidays (published on December 12, 2021 on covid19-modeling.ru), almost coincided with what happened in reality (the error was 0,2%). Tab. 3, illustr. 6, bibliogr. 33.
Keywords: agent oriented model, COVID-19, inverse problem, optimization, regularization, scenario
Mots-clés : index of virus reproduction. 
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     author = {O. I. Krivorotko and S. I. Kabanikhin and M. A. Bektemesov and M. I. Sosnovskaya and A. V. Neverov},
     title = {Simulation of {COVID-19} propagation scenarios in~the {Republic} of {Kazakhstan} based~on~regularization of agent model},
     journal = {Diskretnyj analiz i issledovanie operacij},
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O. I. Krivorotko; S. I. Kabanikhin; M. A. Bektemesov; M. I. Sosnovskaya; A. V. Neverov. Simulation of COVID-19 propagation scenarios in~the Republic of Kazakhstan based~on~regularization of agent model. Diskretnyj analiz i issledovanie operacij, Tome 30 (2023) no. 1, pp. 40-66. http://geodesic.mathdoc.fr/item/DA_2023_30_1_a2/