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Recursive state and parameter estimation of COVID-19 circulating variants dynamics.

Daniel Martins Silva1, Argimiro Resende Secchi2

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This study developed a SEIR-based model with dynamic feedback estimation to track COVID-19 variants. The model accurately detected variant impacts on transmissibility and lethality, aiding pandemic response.

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Area of Science:

  • Epidemiology
  • Control Theory
  • Computational Biology

Background:

  • Non-pharmaceutical interventions are crucial for COVID-19 pandemic control, but their effectiveness is challenged by virus mutations and changing public behavior.
  • Accurate, long-term predictions are essential for effective epidemic control strategies.
  • Dynamic modeling is needed to adapt to evolving epidemic dynamics, including the emergence of new SARS-CoV-2 variants.

Purpose of the Study:

  • To propose and evaluate a SEIR-based model with dynamic feedback estimation for real-time tracking of COVID-19 dynamics.
  • To assess the impact of SARS-CoV-2 variants (Zeta and Gamma) on disease transmissibility and lethality.
  • To investigate the utility of advanced estimation techniques for characterizing epidemic changes and variant emergence.

Main Methods:

  • A SEIR (Susceptible-Exposed-Infectious-Recovered) model was enhanced with augmented state estimation techniques.
  • Constrained Extended Kalman Filter (CEKF), CEKF and Smoother (CEKF & S), and Moving Horizon Estimator (MHE) were implemented for state and parameter estimation.
  • Google mobility data was used to quantify social distancing measures, and vaccine efficacy data informed parameter estimation.

Main Results:

  • The model accurately estimated changes in transmissibility and lethality associated with the Zeta and Gamma variants.
  • Lethality increased by 11-30% for Zeta and 44-107% for Gamma; transmissibility rose by 10-37% for Zeta and 43-119% for Gamma.
  • Parameter estimation revealed temporal variations in underreporting of hospitalizations and deaths, highlighting data challenges.

Conclusions:

  • The proposed dynamic feedback estimation strategy is effective for real-time detection and characterization of circulating SARS-CoV-2 variants.
  • The model provides valuable insights for adapting public health interventions in response to evolving epidemic conditions.
  • This approach supports informed decision-making for pandemic management by dynamically assessing variant impacts.