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

被引:6
作者
Ma, Baoshan [1 ]
Qi, Jishuang [1 ]
Wu, Yiming [1 ]
Wang, Pengcheng [2 ]
Li, Di [3 ]
Liu, Shuxin [4 ]
机构
[1] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China
[2] Univ Houston, Dept Mech Engn, Houston, TX 77204 USA
[3] Dalian Med Univ, Dalian Municipal Cent Hosp, Dept Neuro Intervent, Dalian 116033, Peoples R China
[4] Dalian Med Univ, Dalian Municipal Cent Hosp, Dept Nephrol, Dalian 116033, Peoples R China
基金
中国国家自然科学基金;
关键词
COVID-19; Mathematical modeling; Quantum-behaved particle swarm; optimization; Parameter estimation; AI;
D O I
10.1016/j.dsp.2022.103577
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 41 条
  • [1] Data-based analysis, modelling and forecasting of the COVID-19 outbreak
    Anastassopoulou, Cleo
    Russo, Lucia
    Tsakris, Athanasios
    Siettos, Constantinos
    [J]. PLOS ONE, 2020, 15 (03):
  • [2] [Anonymous], 2020, LANCET, V395, P922, DOI 10.1016/S0140-6736(20)30644-9
  • [3] Fractional-Order SEIQRDP Model for Simulating the Dynamics of COVID-19 Epidemic
    Bahloul, Mohamed A.
    Chahid, Abderrazak
    Laleg-Kirati, Taous-Meriem
    [J]. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY, 2020, 1 : 249 - 256
  • [4] Model-informed COVID-19 vaccine prioritization strategies by age and serostatus
    Bubar, Kate M.
    Reinholt, Kyle
    Kissler, Stephen M.
    Lipsitch, Marc
    Cobey, Sarah
    Grad, Yonatan H.
    Larremore, Daniel B.
    [J]. SCIENCE, 2021, 371 (6532) : 916 - +
  • [5] A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact
    Chamola, Vinay
    Hassija, Vikas
    Gupta, Vatsal
    Guizani, Mohsen
    [J]. IEEE ACCESS, 2020, 8 : 90225 - 90265
  • [6] Chinazzi M, 2020, SCIENCE, V368, P395, DOI [10.1101/2020.02.09.20021261, 10.1126/science.aba9757]
  • [7] coronavirus, J HOPKINS U CTR SYST
  • [8] Epidemiological and clinical characteristics of the COVID-19 epidemic in Brazil
    de Souza, William Marciel
    Buss, Lewis Fletcher
    Candido, Darlan da Silva
    Carrera, Jean-Paul
    Li, Sabrina
    Zarebski, Alexander E.
    Pereira, Rafael Henrique Moraes
    Prete, Carlos A., Jr.
    de Souza-Santos, Andreza Aruska
    Parag, Kris V.
    Belotti, Maria Carolina T. D.
    Vincenti-Gonzalez, Maria F.
    Messina, Janey
    da Silva Sales, Flavia Cristina
    Andrade, Pamela dos Santos
    Nascimento, Vitor Heloiz
    Ghilardi, Fabio
    Abade, Leandro
    Gutierrez, Bernardo
    Kraemer, Moritz U. G.
    Braga, Carlos K. V.
    Aguiar, Renato Santana
    Alexander, Neal
    Mayaud, Philippe
    Brady, Oliver J.
    Marcilio, Izabel
    Gouveia, Nelson
    Li, Guangdi
    Tami, Adriana
    de Oliveira, Silvano Barbosa
    Porto, Victor Bertollo Gomes
    Ganem, Fabiana
    de Almeida, Walquiria Aparecida Ferreira
    Fantinato, Francieli Fontana Sutile Tardetti
    Macario, Eduardo Marques
    de Oliveira, Wanderson Kleber
    Nogueira, Mauricio L.
    Pybus, Oliver G.
    Wu, Chieh-Hsi
    Croda, Julio
    Sabino, Ester C.
    Faria, Nuno Rodrigues
    [J]. NATURE HUMAN BEHAVIOUR, 2020, 4 (08) : 856 - +
  • [9] A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic
    Friji, Hamdi
    Hamadi, Raby
    Ghazzai, Hakim
    Besbes, Hichem
    Massoud, Yehia
    [J]. IEEE ACCESS, 2021, 9 (09): : 13266 - 13285
  • [10] Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy
    Giordano, Giulia
    Blanchini, Franco
    Bruno, Raffaele
    Colaneri, Patrizio
    Di Filippo, Alessandro
    Di Matteo, Angela
    Colaneri, Marta
    [J]. NATURE MEDICINE, 2020, 26 (06) : 855 - +