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

ABSTRACT: 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 susceptible-exposed-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

SUBMITTER: Ma B 

PROVIDER: S-EPMC9067002 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature