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

• Published in: Journal of applied and industrial mathematics Vol. 17; no. 1; pp. 94 - 109
• Main Authors: Krivorotko, O. I., Kabanikhin, S. I., Bektemesov, M. A., Sosnovskaya, M. I., Neverov, A. V.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.03.2023; Springer Nature B.V
• Subjects: Age; Agent-based models; Algorithms; Computational mathematics; Coronaviruses; COVID-19 vaccines; Disease transmission; Epidemics; Global optimization; Infections; Infectious diseases; Inverse problems; Machine learning; Mathematics; Mathematics and Statistics; Open source software; Parameters; Pharmaceuticals; Population; Regularization; Social distancing; Software packages; Time series; France; Kazakhstan; United States > US; COVID-19; inverse problem; agent-based model; optimization; scenario; regularization; basic reproduction number
• ISSN: 1990-4789; 1990-4797; 1990-4797
• DOI: 10.1134/S1990478923010118

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%).