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Extended SEIQR type model for COVID-19 epidemic and data analysis
Swarnali Sharma1, Vitaly Volpert2,3,4, Malay Banerjee5
1Department of Mathematics, Vijaygarh Jyotish Ray College, Kolkata - 700032, India.
Mathematical Biosciences and Engineering : MBE
|December 31, 2020
Summary
This study models COVID-19 using an extended SEIQR framework, revealing that low social interaction (K=0.1) slows disease spread. A small increase in social contact could trigger a significant epidemic surge.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- COVID-19 pandemic necessitates robust epidemiological models.
- Understanding disease transmission dynamics is crucial for public health interventions.
Purpose of the Study:
- To develop and apply an extended SEIQR model for COVID-19.
- To investigate the impact of social interaction on disease progression.
- To estimate key epidemiological parameters for European countries.
Main Methods:
- Utilized an extended SEIQR (Susceptible-Exposed-Infected-Quarantined-Recovered) compartmental model.
- Incorporated subclasses of susceptible individuals to quantify social interaction effects.
- Fitted model parameters to available COVID-19 data from European nations.
Main Results:
- The model determined the basic reproduction number and final epidemic size.
- Social interaction coefficient (K) estimated at approximately 0.1 in studied European countries, indicating slow progression.
- A minor rise in K could precipitate a substantial epidemic increase.
Conclusions:
- The SEIQR model effectively captures COVID-19 dynamics.
- Social interaction significantly influences epidemic trajectory.
- Current low levels of social contact may be masking potential for rapid resurgence.
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