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Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples
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Recursive state and parameter estimation of COVID-19 circulating variants dynamics.
Daniel Martins Silva1, Argimiro Resende Secchi2
1Chemical Engineering Program/COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-942, Brazil. dmsilva@peq.coppe.ufrj.br.
Scientific Reports
|September 23, 2022
Summary
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.
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.
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