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

DOI10.1515/jiip-2021-0038zbMath1515.92078OpenAlexW4362587915MaRDI QIDQ6112103

Mariia Sosnovskaia, Olga I. Krivorotko, Sergey I. Kabanikhin

Publication date: 7 July 2023

Published in: Journal of Inverse and Ill-Posed Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jiip-2021-0038

zbMATH Keywords

optimizationinverse problemregularizationdata analysisforecastingidentifiabilityCOVID-19Covasim softwareOPTUNA

Mathematics Subject Classification ID

Epidemiology (92D30)

Related Items

Sensitivity of MFG SEIR-HCD epidemiological model, Artificial intelligence for COVID-19 spread modeling

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Numerical-statistical and analytical study of asymptotics for the average multiplication particle flow in a random medium
• A time delay dynamical model for outbreak of 2019-nCov and the parameter identification
• Logistic equation and COVID-19
• Diffusion-reaction compartmental models formulated in a continuum mechanics framework: application to COVID-19, mathematical analysis, and numerical study
• COVID-19: data-driven mean-field-type game perspective
• Mean field game for modeling of COVID-19 spread
• A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
• On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics
• Sensitivity analysis and practical identifiability of some mathematical models in biology
• Controlling Propagation of Epidemics via Mean-Field Control
• Identifiability and observability analysis for experimental design in nonlinear dynamical models
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates