TY - JOUR

T1 - Agent-based mathematical model of COVID-19 spread in Novosibirsk region: Identifiability, optimization and forecasting

AU - Krivorotko, Olga

AU - Sosnovskaia, Mariia

AU - Kabanikhin, Sergey

N1 - This research is supported by the Russian Foundation for Basic Research, the Royal Society of London (project no. 20-51-10003) – investigation of the inverse problem for agent-based model (Sections 3, 4 and 5), by the Council for Grants of the President of the Russian Federation (project no. MK-4994.2021.1.1) – data analysis (Section 2), and by the Mathematical Center in Akademgorodok under the agreement No. 075-15-2022-281 with the Ministry of Science and Higher Education of the Russian Federation – short history review and analysis of numerical results.

PY - 2023/6/1

Y1 - 2023/6/1

N2 - 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 spread modeling. 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.

AB - 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 spread modeling. 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.

KW - COVID-19

KW - Covasim software

KW - OPTUNA

KW - data analysis

KW - forecasting

KW - identifiability

KW - inverse problem

KW - optimization

KW - regularization

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85151833294&origin=inward&txGid=e0abf4972aa4ef9244c3b13a1b51e7dc

UR - https://www.mendeley.com/catalogue/26c3261b-cb4a-3300-973a-7b4c708c4a11/

U2 - 10.1515/jiip-2021-0038

DO - 10.1515/jiip-2021-0038

M3 - Article

VL - 31

SP - 409

EP - 425

JO - Journal of Inverse and Ill-Posed Problems

JF - Journal of Inverse and Ill-Posed Problems

SN - 0928-0219

IS - 3

ER -