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Linear parameter varying model of COVID-19 pandemic exploiting basis functions
Roozbeh Abolpour1, Sara Siamak1, Mohsen Mohammadi2
1School of Electrical and Computer Engineering, Shiraz University, Iran.
Biomedical Signal Processing and Control
|July 26, 2021
Summary
This study introduces the SEMHIRD model to track COVID-19 spread, dividing people into Susceptible, Exposed, Minor infected, Hospitalized, Intensive infected, Recovered, and Deceased categories. It analyzes Italy
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- COVID-19 pandemic poses a significant global health threat.
- Understanding disease transmission dynamics is crucial for effective intervention.
- Existing models may not fully capture the nuances of disease progression and societal impact.
Purpose of the Study:
- To develop and analyze a novel conceptual model (SEMHIRD) for COVID-19 pandemic dynamics.
- To estimate time-varying model parameters using real-world data.
- To investigate the impact of social distancing and quarantine measures on disease outcomes.
Main Methods:
- Development of the Susceptible, Exposed, Minor infected, Hospitalized, Intensive infected, Recovered, and Deceased (SEMHIRD) model.
- Utilizing real-world data from Italy for parameter estimation.
- Employing linear least squares and basis functions to estimate time-varying parameters.
- Derivation and stability analysis of a Linear Parameter Varying (LPV) model.
Main Results:
- The SEMHIRD model effectively captures COVID-19 transmission dynamics in Italy.
- Time-varying parameters were estimated, reflecting changes in disease spread over time.
- The LPV model's stability was analyzed.
- Simulations demonstrated the influence of social distancing and quarantine on hospitalization rates.
Conclusions:
- The SEMHIRD model provides a robust framework for understanding and predicting COVID-19 trajectories.
- Dynamic parameter estimation is essential for adapting models to evolving pandemic conditions.
- Social distancing and quarantine interventions significantly impact the severity of COVID-19 cases.
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