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

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%). © 2023, Pleiades Publishing, Ltd.