SQEIR: An epidemic virus spread analysis and prediction model

https://doi.org/10.1016/j.compeleceng.2022.108230Get rights and content

Highlights

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    New infectious disease SQEIR model proposed to predict virus spread.

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    Optimal estimation of the number of infected individuals by using the least squares method.

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    Formulation of g a new differential equation to describe the population distribution.

Abstract

In 2019, a new strain of coronavirus pneumonia spread quickly worldwide. Viral propagation may be simulated using the Susceptible Infectious Removed (SIR) model. However, the SIR model fails to consider that separation of patients in the COVID-19 incubation stage entails difficulty and that these patients have high transmission potential. The model also ignores the positive effect of quarantine measures on the spread of the epidemic. To address the two flaws in the SIR model, this study proposes a new infectious disease model referred to as the Susceptible Quarantined Exposed Infective Removed (SQEIR) model. The proposed model uses the weighted least squares for the optimal estimation of important parameters in the infectious disease model. Based on these parameters, new differential equations were developed to describe the spread of the epidemic. The experimental results show that this model exhibits an accuracy 6.7% higher than that of traditional infectious disease models.

Keywords

Susceptible quarantined exposed infective
Infectious disease model
Mathematical model
COVID-19

Data Availability

  • No data was used for the research described in the article.

Cited by (0)

Yichun Wu graduated from Hengyang Normal University with a bachelor's degree in 2021. She is currently studying at Hengyang Normal University, School of Computer Science and Technology, majoring in Electronics and Communication Engineering for a master's degree. Her current research interests include image style transfer and video image style transfer.

Yaqi Sun received her M.S. degree in Control Engineering from Guilin University of Technology, China in 2013. She is currently an Assistant Professor at the College of Computer Science and Technology, Hengyang Normal University. Her current research interests include machine learning and image processing.

Mugang Lin received his Ph.D. degree from Central South University, Changsha, China in 2019. He is currently an Associate Professor at the College of Computer Science and Technology, Hengyang Normal University, Hengyang, China and at the Hunan Provincial Key Laboratory of Intelligent Information Processing and Application. His current research interests include computer algorithms and parameterized algorithms.

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