Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting Full article
| Journal |
Journal of Inverse and Ill-Posed Problems
ISSN: 0928-0219 , E-ISSN: 1569-3945 |
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| Output data | Year: 2023, Volume: 31, Number: 3, Pages: 1-17 Pages count : 17 DOI: 10.1515/jiip-2021-0038 | ||||
| Tags | COVID-19, data analysis, inverse problem, optimization, forecasting, Covasim software, regularization, identifiability, OPTUNA | ||||
| Authors |
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| Affiliations |
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Funding (3)
| 1 | Russian Foundation for Basic Research | 20-51-10003 |
| 2 | Президент РФ | МК-4994.2021.1.1 |
| 3 |
Министерство науки и высшего образования РФ Mathematical Center in Akademgorodok |
075-15-2019-1613, 075-15-2022-281 |
Abstract:
The problem of identification of unknown epidemiological parameters (contagiosity, the initial number of infected individuals, probability of being tested) of an agent-based model of COVID-19 spread in Novosibirsk region is solved and analyzed. The first stage of modeling involves data analysis based on the machine learning approach that allows one to determine correlated datasets of performed PCR tests and number of daily diagnoses and detect some features (seasonality, stationarity, data correlation) to be used for COVID-19 spreadmodeling. At the second stage, the unknown model parameters that depend on the date of introducing of containment measures are calibrated with the usage of additional measurements such as the number of daily diagnosed and tested people using PCR, their daily mortality rate and other statistical information about the disease. The calibration is based on minimization of the misfit function for daily diagnosed data. The OPTUNA optimization framework with tree-structured Parzen estimator and covariance matrix adaptation evolution strategy is used to minimize the misfit function. Due to ill-posedness of identification problem, the identifiability analysis is carried out to construct the regularization algorithm. At the third stage, the identified parameters of COVID-19 for Novosibirsk region and different scenarios of COVID-19 spread are analyzed in relation to introduced quarantine measures. This kind of modeling can be used to select effective anti-pandemic programs.
Cite:
Krivorotko O.
, Sosnovskaia M.
, Kabanikhin S.
Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting
Journal of Inverse and Ill-Posed Problems. 2023. V.31. N3. P.1-17. DOI: 10.1515/jiip-2021-0038 WOS Scopus РИНЦ OpenAlex
Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting
Journal of Inverse and Ill-Posed Problems. 2023. V.31. N3. P.1-17. DOI: 10.1515/jiip-2021-0038 WOS Scopus РИНЦ OpenAlex
Dates:
| Submitted: | Jun 25, 2021 |
| Accepted: | Jan 30, 2023 |
| Published print: | Apr 4, 2023 |
| Published online: | Apr 4, 2023 |
Identifiers:
| Web of science: | WOS:000962523800001 |
| Scopus: | 2-s2.0-85151833294 |
| Elibrary: | 61124408 |
| OpenAlex: | W4362587915 |