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Zhang J, Zheng N, Liu M, Yao D, Wang Y, Wang J, Xin J. Multi-weight susceptible-infected model for predicting COVID-19 in China. Neurocomputing 2023; 534:161-170. [PMID: 36923265 PMCID: PMC9993734 DOI: 10.1016/j.neucom.2023.02.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/10/2023] [Accepted: 02/26/2023] [Indexed: 03/17/2023]
Abstract
The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3-4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022.
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Affiliation(s)
- Jun Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Mingyu Liu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Dingyi Yao
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China.,Qian Xuesen College, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Yusong Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jianji Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
| | - Jingmin Xin
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
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Abbes A, Ouannas A, Shawagfeh N, Grassi G. The effect of the Caputo fractional difference operator on a new discrete COVID-19 model. RESULTS IN PHYSICS 2022; 39:105797. [PMID: 35818497 PMCID: PMC9259007 DOI: 10.1016/j.rinp.2022.105797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/27/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
This study aims to generalize the discrete integer-order SEIR model to obtain the novel discrete fractional-order SEIR model of COVID-19 and study its dynamic characteristics. Here, we determine the equilibrium points of the model and discuss the stability analysis of these points in detail. Then, the non-linear dynamic behaviors of the suggested discrete fractional model for commensurate and incommensurate fractional orders are investigated through several numerical techniques, including maximum Lyapunov exponents, phase attractors, bifurcation diagrams and C 0 algorithm. Finally, we fitted the model with actual data to verify the accuracy of our mathematical study of the stability of the fractional discrete COVID-19 model.
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Affiliation(s)
- Abderrahmane Abbes
- Department of Mathematics, The University of Jordan, Amman, 11942, Jordan
| | - Adel Ouannas
- Department of Mathematics and Computer Science, University of Larbi Ben M'hidi, Oum El Bouaghi, 04000, Algeria
| | - Nabil Shawagfeh
- Department of Mathematics, The University of Jordan, Amman, 11942, Jordan
| | - Giuseppe Grassi
- Dipartimento Ingegneria Innovazione, Universita del Salento, Lecce, 73100, Italy
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Ma J, Lin W. Dynamics of a stochastic COVID-19 epidemic model considering asymptomatic and isolated infected individuals. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5169-5189. [PMID: 35430859 DOI: 10.3934/mbe.2022242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Coronavirus disease (COVID-19) has a strong influence on the global public health and economics since the outbreak in 2020. In this paper, we study a stochastic high-dimensional COVID-19 epidemic model which considers asymptomatic and isolated infected individuals. Firstly we prove the existence and uniqueness for positive solution to the stochastic model. Then we obtain the conditions on the extinction of the disease as well as the existence of stationary distribution. It shows that the noise intensity conducted on the asymptomatic infections and infected with symptoms plays an important role in the disease control. Finally numerical simulation is carried out to illustrate the theoretical results, and it is compared with the real data of India.
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Affiliation(s)
- Jiying Ma
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Wei Lin
- College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
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