Multi-stage COVID-19 Epidemic Modeling Based on PSO and SEIR

Autor:	Ben Niu, Jinsong Chen, Haiyun Qiu
Rok vydání:	2021
Předmět:	Set (abstract data type) Mathematical optimization Transmission (telecommunications) Computer science Genetic algorithm Convergence (routing) Key (cryptography) Particle swarm optimization Computational intelligence Swarm intelligence
Zdroj:	Lecture Notes in Computer Science ISBN: 9783030787424 ICSI (1)
DOI:	10.1007/978-3-030-78743-1_24
Popis:	In this study, based on the characteristics and the transmission mechanism of COVID-19, SEIR epidemiological model is employed for modeling and analysis, utilizing the data of Hubei Province. To optimize the key epidemic parameters of the proposed SEIR model, a stochastic computational intelligence approach, the Particle Swarm Optimization (PSO) is introduced. To better analyze the epidemic, the data between January 20, 2020 to March 25, 2020 is selected and divided into four stages. The parameters are dynamically changeable at different stages of the epidemic, which shows the effectiveness of public health prevention and control measures. Moreover, the Genetic Algorithm (GA) and the Bacterial Foraging Optimization (BFO) are also executed for comparison. The experimental results demonstrate that all swarm intelligence algorithms mentioned above can help forecast COVID-19, and PSO shows the advantages of faster convergence speed and the capability of finding a better set of solutions in fewer iterations, particularly.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b7f0beeef4d14c64e0e576530868f1a1 https://doi.org/10.1007/978-3-030-78743-1_24 Zobrazit plný text záznamu