Volume 30, No 1, 2023, P. 40-66
UDC 519.8+518.25
O. I. Krivorotko, S. I. Kabanikhin, M. A. Bektemesov, M. I. Sosnovskaya, and A. V. Neverov
Simulation of COVID-19 propagation scenarios in the Republic of Kazakhstan based on regularization of agent model
Abstract:
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 ABMis 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, index of virus reproduction.
DOI: 10.33048/daio.2023.30.746
Olga I. Krivorotko 1,2,3
Sergey I. Kabanikhin 2,3
Maktagali A. Bektemesov 4
Mariya I. Sosnovskaya 3
Andrey V. Neverov 1,3
1. Institute of Computational Mathematics and Mathematical Geophysics,
6 Acad. Lavrentiev Avenue, 630090 Novosibirsk, Russia
2. Sobolev Institute of Mathematics,
4 Acad. Koptyug Avenue, 630090 Novosibirsk, Russia
3.
Novosibirsk State University,
2 Pirogov St., 630090 Novosibirsk, Russia
4. Abai Kazakh National Pedagogical University,
13 Dostyk Avenue, 050010 Almaty, Kazakhstan
e-mail: krivorotko.olya@mail.ru, ksi52@mail.ru, maktagali@mail.ru, m.sosnovskaya@alumni.nsu.ru, a.neverov@g.nsu.ru
Received July 4, 2022
Revised September 27, 2022
Accepted September 28, 2022
References
[1] V. A. Adarchenko, S. A. Baban’, A. A. Bragin, [et al.]. Modelling the coronavirus epidemic development with the use of differential and statistical models (Snezhinsk, 2020) (Prepr. RFYaTs—VNIITF, No. 264) [Russian].
[2] O. I. Krivorotko and S. I. Kabanikhin, Mathematical models for the spread of COVID-19 (Novosibirsk, 2022) (Prepr. Inst. Mat. Sobolev., No. 300) [Russian].
[3] C. C. Kerr, R. M. Stuart, D. Mistry, [et al.]. Covasim: An agent-based model of COVID-19 dynamics and interventions, PLoS Comput. Biol. 17 (7), ID e1009149, 32 p. (2021).
[4] R. Laubenbacher, F. Hinkelmann, andM. Oremland, Agent-based models and optimal control in biology: A discrete approach, in Mathematical Concepts and Methods in Modern Biology: Using Modern Discrete Models, Ch. 5 (Acad. Press, San Diego, CA, 2013), pp. 143–178.
[5] A. I. Vlad, T. E. Sannikova, and A. A. Romanyukha, Modelling the spread of respiratory viral infections in a city: Multi-agent approach, Mat. Biol. Bioinform. 15 (2), 338–356 (2020) [Russian].
[6] A. Aleta, D. Martín-Corral, A. Pastore y Piontti, [et al.]. Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19, Nat. Hum. Behav. 4 (9), 964–971 (2020).
[7] M. S. Y. Lau, B. Grenfell, M. Thomas, M. Bryan, K. Nelson, and B. Lopman, Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA, PNAS 117 (36), 22430–22435 (2020).
[8] A. J. Kucharski, P. Klepac, A. J. K. Conlan, [et al.]. Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: A mathematical modelling study, Lancet Infect. Dis. 20 (10), 1151–1160 (2020).
[9] N. Hoertel, M. Blachier, C. Blanco, [et al.]. A stochastic agent-based model of the SARS-CoV-2 epidemic in France, Nat. Med. 26 (9), 1417–1421 (2020).
[10] J. Hellewell, S. Abbott, A. Gimma, [et al.]. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts, Lancet Glob. Health. 8 (4), e488–e496 (2020).
[11] B. F. Nielsen, L. Simonsen, and K. Sneppen, COVID-19 superspreading suggests mitigation by social network modulation, Phys. Rev. Lett. 126 (11), ID 118301, 6 p. (2021).
[12] COVID-19 Agent-Based Simulator (Inst. Disease Modeling, Bellevue, WA, 2022).
Available at github.com/InstituteforDiseaseModeling/covasim/ (accessed Jan. 9, 2023).
[13] N. B. Noll, I. Aksamentov, V. Druelle, [et al.]. COVID-19 scenarios: An interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2 (Cold Spring Harbor Lab., Oyster Bay, NY, 2020). (Prepr. Server Health Sci. medRxiv).
Available at medrxiv.org/content/10.1101/2020.05.05.20091363 (accessed Jan. 9, 2023).
[14] J. T. Tuomisto, J. Yrjölä, M. Kolehmainen, J. Bonsdorff, J. Pekkanen, and T. Tikkanen, An agent-based epidemic model REINA for COVID-19 to identify destructive policies (Cold Spring Harbor Lab., Oyster Bay, NY, 2020). (Prepr. Server Health Sci. medRxiv).
Available at medrxiv.org/content/10.1101/2020.04.09.20047498 (accessed Jan. 9, 2023).
[15] O. I. Krivorotko, S. I. Kabanikhin, N. Yu. Zyatkov, A. Yu. Prikhodko, N. M. Prokhoshin, and M. A. Shishlenin, Mathematical modeling and forecasting of COVID-19 in Moscow and Novosibirsk region, Sib. Zh. Vychisl. Mat. 23 (4), 395–414 (2020) [Russian] [Num. Anal. Appl. 13 (4), 332–348 (2020)].
[16] O. I. Krivorotko, M. I. Sosnovskaia, I. A. Vashchenko, C. C. Kerr, and D. Lesnic, Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm, Infect. Dis. Model. 7 (1), 30–44 (2022).
[17] API Reference — Pandas 1.5.2 Documentation (NumFOCUS, Austin, TX, 2022).
Available at pandas.pydata.org/docs/reference/ (accessed Jan. 9, 2023).
[18] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco, 1970; Mir, Moscow, 1974 [Russian]).
[19] P. P. Dabral and M. Z. Murry, Modelling and forecasting of rainfall time series using SARIMA, Environ. Process. 4 (2), 399–419 (2017).
[20] D. A. Dickey and W. A. Fuller, Distribution of the estimators for autoregressive time series with a unit root, J. Am. Stat. Assoc. 74 (366a), 427–431 (1979).
[21] SciPy API — SciPy v1.10.0 Manual (Enthought, Austin, TX, 2022).
Available at docs.scipy.org/doc/scipy/reference/ (accessed Jan. 9, 2023).
[22] Household: Size & Composition, 2022 (United Nations, New York, 2022).
Available at population.un.org/Household/#/countries/840 (accessed Jan. 9, 2023).
[23] SynthPops — create synthetic populations for COVID-19 epidemic analyses (Inst. Disease Modeling, Bellevue, WA, 2021).
Available at github.com/InstituteforDiseaseModeling/synthpops/ (accessed Jan. 9, 2023).
[24] S. A. Lauer, K. H. Grantz, Q. Bi, [et al.]. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: Estimation and application, Ann. Intern. Med. 172 (9), 577–582 (2020).
[25] R. Wölfel, V. M. Corman, W. Guggemos, [et al.]. Virological assessment of hospitalized patients with COVID-2019, Nature 581 (7809), 465–469 (2020).
[26] R. Verity, L. C. Okell, I. Dorigatti, [et al.]. Estimates of the severity of coronavirus disease 2019: A model-based analysis, Lancet Infect. Dis. 20 (6), 669–677 (2020).
[27] D. Wang, B. Hu, C. Hu, [et al.]. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China, JAMA 323 (11), 1061–1069 (2020).
[28] O. I. Krivorotko, S. I. Kabanikhin, M. I. Sosnovskaya, and D. V. Andornaya, Sensitivity and identifiability analysis of COVID-19 pandemic models, Vavilov. Zh. Genet. Sel. 25 (1), 82–91 (2021) [Russian].
[29] O. I. Krivorotko, M. I. Sosnovskaya, and I. A. Vashchenko, Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting, J. Inverse Ill-Posed Probl. [in print].
[30] Optuna — A Hyperparameter Optimization Framework (Preferred Networks, Tokyo, 2018).
Available at optuna.org (accessed Jan. 9, 2023).
[31] A. A. Zhiglyavskii and A. G. Zhilinskas, Methods for Global Extremum Search (Moscow, Nauka, 1991).
[32] J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, Algorithms for hyperparameter optimization, in Advances in Neural Information Processing Systems 24 (25th Annual Conf., Granada, Spain, Dec. 12–15, 2011), Vol. 3 (Curran Associates, Red Hook, NY, 2012), pp. 2546–2554.
[33] M. L. Daza-Torres, M. A. Capistrán, A. Capella, and J. Andrés Christen, Bayesian sequential data assimilation for COVID-19 forecasting, Epidemics 39, ID 100564, 10 p. (2022).
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