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Sensitivity analysis and optimal control of COVID-19 dynamics based on SEIQR model
Takasar Hussain1, Muhammad Ozair1, Farhad Ali2
1Department of Mathematics, COMSATS University Islamabad, Attock Campus, Attock, Pakistan.
Results in Physics
|February 24, 2021
Summary
This study introduces an SEIQR model to predict COVID-19 outbreaks and analyze prevention effects. The model accurately forecasts cases and discusses future scenarios based on implemented restrictions.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Understanding epidemic dynamics and prediction is crucial for managing outbreaks like COVID-19.
- The need for robust models to assess prevention strategies and forecast disease spread is evident.
Purpose of the Study:
- To design and analyze an SEIQR model for the COVID-19 pandemic.
- To calculate key epidemiological parameters and predict future case numbers.
- To evaluate the impact of prevention methods on outbreak trajectories.
Main Methods:
- Development of a compartmental SEIQR (Susceptible-Exposed-Infectious-Quarantined-Recovered) model.
- Analytical calculation of the basic reproduction number (R0) and equilibrium states.
- Parameter calibration using registered case data for predictions, including simulations.
Main Results:
- The SEIQR model accurately reflects registered COVID-19 cases.
- Predictions for new cases in Pakistan were generated through parameter calibration.
- Analytical and simulation methods determined current and permanent levels of COVID-19 cases.
Conclusions:
- The SEIQR model provides a valuable tool for epidemic prediction and understanding COVID-19 dynamics.
- The study highlights the potential impact of precautionary restrictions on future pandemic scenarios.
- Model findings offer insights for public health interventions and resource allocation.
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