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

The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptibleexposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

The outbreak of COVID-19 has posed unprecedented challenges worldwide. Parametric modeling and analysis are crucial in understanding and controlling the pandemic, with the proposed improved quantum-behaved particle swarm optimization algorithm showing good accuracy and convergence when estimating parameters of the SEIR model.