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Kalman-based compartmental estimation for covid-19 pandemic using advanced epidemic model
Sumanta Kumar Nanda1, Guddu Kumar1, Vimal Bhatia1,2
1Department of Electrical Engineering, Indian Institute of Technology Indore, Indore, India.
Biomedical Signal Processing and Control
|March 6, 2023
Summary
This study introduces a new SEIRPV compartmental model for COVID-19, enhancing epidemic modeling with exposed, recovered, deceased, and vaccinated states. The model, utilizing a cubature Kalman filter, offers improved quantitative insights for public health interventions.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- Effective COVID-19 prevention relies on quantitative data regarding transmission factors.
- Existing SIR models offer a basic framework but lack detailed compartments for comprehensive analysis.
Purpose of the Study:
- Introduce a novel SEIRPV compartmental model for COVID-19.
- Enhance the quantitative basis for administrative public health measures.
- Stochastically model exposed, infected, and vaccinated populations within a unified framework.
Main Methods:
- Developed a nonlinear, stochastic SEIRPV (Susceptible, Exposed, Infected, Recovered-exposed, Recovered-infected, Passed away, Vaccinated) model.
- Employed the cubature Kalman filter (CKF) for nonlinear estimation of compartmental populations.
- Analyzed model properties including stability, equilibrium, and reproduction rate.
Main Results:
- The SEIRPV model provides a more detailed and stochastic representation of COVID-19 dynamics.
- CKF demonstrated accurate estimation with manageable computational cost.
- Model performance was validated using real-world COVID-19 outbreak data.
Conclusions:
- The proposed SEIRPV model offers a significant advancement in COVID-19 epidemic modeling.
- This enhanced modeling approach can strengthen the practicality and effectiveness of public health interventions.
- The study highlights the value of integrating stochasticity and detailed compartments for disease control.
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