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Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization

Baoshan Ma1, Jishuang Qi1, Yiming Wu1

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, 116026, China.

Digital Signal Processing
|May 9, 2022
PubMed
Summary

This study introduces an improved quantum-behaved particle swarm optimization (QPSO) algorithm for accurate COVID-19 transmission modeling. The enhanced QPSO method improves parameter estimation for better pandemic control strategies.

Keywords:
COVID-19Mathematical modelingParameter estimationQuantum-behaved particle swarm optimization

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Optimization Algorithms

Background:

  • The COVID-19 pandemic presents a significant global challenge.
  • Accurate epidemiological modeling is crucial for understanding and controlling infectious disease outbreaks.
  • Effective COVID-19 transmission models rely on precise parameter identification.

Purpose of the Study:

  • To propose an improved quantum-behaved particle swarm optimization (QPSO) algorithm for estimating parameters in epidemiological models.
  • To enhance the accuracy and global search capacity of particle swarm optimization for infectious disease modeling.
  • To provide a robust framework for analyzing epidemic characteristics and informing control measures.

Main Methods:

  • Extension of the susceptible-exposed-infectious-recovered (SEIR) epidemiological model.
  • Development of an improved QPSO algorithm with novel strategies for updating weighting factors and increasing particle diversity.
  • Comparative analysis against state-of-the-art estimation algorithms using real-world epidemic datasets from China, Italy, and the US.

Main Results:

  • The proposed QPSO algorithm demonstrates high accuracy and effective convergence in parameter estimation for the SEIR model.
  • The enhanced QPSO method achieves comparable computational complexity to existing algorithms.
  • The algorithm successfully identified key parameters for COVID-19 transmission dynamics.

Conclusions:

  • The improved QPSO algorithm offers a powerful tool for accurate epidemiological parameter estimation.
  • This framework aids experts in understanding epidemic development and formulating effective prevention and control strategies.
  • The study highlights the importance of advanced optimization techniques in managing public health crises like COVID-19.