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基于分阶段SEIAHRD模型的传染病预测分析——基于ACPSO优化算法
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Abstract:
为提高流行病模型预测精度，探索更有效的传染病防控措施，本文构建了分阶段带有潜伏、住院和无症状感染仓室的SEIAHRD动力学模型。利用自适应混沌粒子群算法(ACPSO)对参数进行优化估计，并对数值进行拟合。结果表明模型SEIAHRD的预测结果与真实值有较强的相关性，相关系数为0.94，可以准确反映流行病传播动态，提升疫情预测精度，应用到实际疫情防控中。
In order to improve the prediction accuracy of epidemic model and explore more effective measures for prevention and control of infectious diseases, a SEIAHRD dynamic model with latent, inpatient and asymptomatic infection compartment in stages was constructed. Adaptive chaotic particle swarm optimization (ACPSO) was used to estimate the parameters and fit the values. The results showed that the prediction results of SEIAHRD model had a strong correlation with the real value, and the correlation coefficient was 0.94, which could accurately reflect the epidemic trans-mission dynamics, improve the accuracy of epidemic prediction, and be applied to the actual epi-demic prevention and control.
| [1] | Kermack, W.O. and McKendrick, A.G. (1927) A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115, 700-721.
https://www.jstor.org/stable/94815 https://doi.org/10.1098/rspa.1927.0118 |
| [2] | Taimoor, M., Ali, S., Shah, I. and Muwanika, F.R. (2022) COVID-19 Pandemic Data Modeling in Pakistan Using Time-Series SIR. Computational and Mathematical Methods in Medicine, 2022, Article ID: 6001876.
https://doi.org/10.1155/2022/6001876 |
| [3] | Cumsille, P., Rojas-Díaz ó., de Espanés, P.M. and Verdugo-Hernandéz, P. (2021) Forecasting COVID-19 Chile’ Second Outbreak by a Generalized SIR Model with Constant Time Delays and a Fitted Positivity Rate. Mathematics and Computers in Simulation, 193, 1-18. https://doi.org/10.1016/j.matcom.2021.09.016 |
| [4] | Marinov, T.T. and Marinova, R.S. (2022) Adaptive SIR Model with Vaccination: Simultaneous Identification of Rates and Functions Illustrated with COVID-19. Scientific Report, 12, Article No. 15688.
https://doi.org/10.1038/s41598-022-20276-7 |
| [5] | Singh, R.A., Lal, R. and Kotti, R.R. (2022) Time-Discrete SIR Model for COVID-19 in Fiji. Epidemiology and Infection, 150, E75. https://doi.org/10.1017/S0950268822000590 |
| [6] | Wang H.W., Wang, Z.Z., Dong, Y.Q., et al. (2020) Phase-Adjusted Estimation of the Number of Coronavirus Disease 2019 Cases in Wuhan, China. Cell Discovery, 6, Arti-cle No. 10. https://doi.org/10.1038/s41421-020-0148-0 |
| [7] | Fang, Y.Q., Nie, Y.T. and Penny, M. (2020) Trans-mission Dynamics of the COVID-19 Outbreak and Effectiveness of Government Interventions: A Data-Driven Analysis. Journal of Medical Virology, 92, 645-659.
https://doi.org/10.1002/jmv.25750 |
| [8] | Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Pro-ceedings of ICNN’95—International Conference on Neural Networks, Perth, 27 November 1995-1 December 1995, 1942-1948.
https://doi.org/10.1109/ICNN.1995.488968 |
| [9] | Navaneeth, B. and Suchetha, M. (2019) PSO Optimized 1-D CNN-SVM Architecture for Real-Time Detection and Classification Applications. Computers in Biology and Medicine, 108, 85-92.
https://doi.org/10.1016/j.compbiomed.2019.03.017 |
| [10] | Zeng, N.Y., Qiu, H., Wang, Z.D., et al. (2018) A New Switching-Delayed-PSO-Based Optimized SVM Algorithm for Diagnosis of Alzheimer’s Disease. Neurocomputing, 320, 195-202. https://doi.org/10.1016/j.neucom.2018.09.001 |
| [11] | 罗丹婷, 钱华, 刘聪, 黄顺祥. 不同国家和时段COVID-19控制策略效果评价: 基于自适应PSO-SEIR模型[J]. 科学通报, 2021, 66(Z1): 453-464. |
| [12] | Aron, J.L. and Schwartz, I.B. (1984) Seasonality and Period-Doubling Bifurcations in an Epidemic Model. Journal of Theoret-ical Biology, 110, 665-679. https://doi.org/10.1016/S0022-5193(84)80150-2 |
| [13] | Batistela, C.M., Correa, D.P.F., Bueno, á.M. and Piqueira, J.R.C. (2020) SIRSi Compartmental Model for COVID-19 Pandemic with Immunity Loss. Chaos, Solitons & Fractals, 142, 110388. https://doi.org/10.1016/j.chaos.2020.110388 |
| [14] | Xin, H.L.,Wang, Z., Feng, S., Sun, Z., et al. (2023) Transmission Dynamics of SARS-CoV-2 Omicron Variant Infections in Hangzhou, Zhejiang, China, January-February 2022. International Journal of Infectious Diseases, 126, 132-135. https://doi.org/10.1016/j.ijid.2022.10.033 |
