SQEIR: An epidemic virus spread analysis and prediction model.
Yichun Wu1, Yaqi Sun1,2, Mugang Lin1,2
1College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China.
Computers & Electrical Engineering : an International Journal
|August 15, 2022
Summary
A new Susceptible Quarantined Exposed Infective Removed (SQEIR) model improves COVID-19 spread prediction by accounting for incubation periods and quarantine measures, showing 6.7% higher accuracy than traditional models.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Infectious Disease Dynamics
Background:
- The 2019 coronavirus pneumonia pandemic highlighted limitations in existing epidemiological models.
- Traditional Susceptible Infectious Removed (SIR) models do not account for the infectiousness of individuals in the incubation stage or the impact of quarantine.
Purpose of the Study:
- To develop an improved infectious disease model that addresses the shortcomings of the SIR model.
- To introduce the Susceptible Quarantined Exposed Infective Removed (SQEIR) model for more accurate epidemic simulation.
Main Methods:
- The study proposes the SQEIR model, incorporating exposed and quarantined compartments.
- Weighted least squares method was utilized for optimal parameter estimation within the model.
- New differential equations were formulated to represent epidemic spread using the SQEIR framework.
Main Results:
- The SQEIR model demonstrated superior accuracy in simulating epidemic spread.
- Experimental results indicated a 6.7% increase in accuracy compared to traditional models.
- The model effectively incorporates the impact of quarantine and the transmission potential of exposed individuals.
Conclusions:
- The SQEIR model offers a more comprehensive approach to simulating infectious disease outbreaks like COVID-19.
- Accurate parameter estimation and inclusion of key epidemiological factors enhance predictive capabilities.
- This enhanced modeling approach can aid public health strategies in managing epidemics.
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