Robust Observer-Based Parameter Identification and State Estimation for COVID-19 Modeling: Analyzing the Omicron Variant in Mexico using Fractional-Order SIR and SEIR Models

https://doi.org/10.58571/CNCA.AMCA.2023.025

Resumen: This work presents a robust observer-based approach for parameter identification and state estimation in fractional-order SIR and SEIR models, applied to analyze the spread of the SARS-CoV-2 Omicron variant in Mexico from December 26, 2021, to March 26, 2022. By utilizing measurements of changes in the infected population, a robust observer is proposed to reconstruct the transmission rate within the fractional model. The method accounts for noisy measurement cases and incorporates parametric reconstruction and state observation. The proposed approach is demonstrated through numerical results, showcasing its capabilities in effectively estimating parameters and states. The findings of this study have implications for formulating strategies to prevent, slow, and halt the spread of infectious diseases, offering valuable insights for combating future disease outbreaks.

¿Cómo citar?

Chavez, Gustavo; Gonzalez-Olvera, Marcos A. Robust Observer-Based Parameter Identification and State Estimation for COVID-19 Modeling: Analyzing the Omicron Variant in Mexico using Fractional-Order SIR and SEIR Models. Memorias del Congreso Nacional de Control Automático, pp. 50-55, 2023. https://doi.org/10.58571/CNCA.AMCA.2023.025

Palabras clave

Modelado e Identificación de Sistemas; Sistemas Biomédicos; Otros Tópicos Afines

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