基于分阶段SEIAHRD模型的传染病预测分析——基于ACPSO优化算法
Prediction Analysis of Infectious Diseases Based on Staged SEIAHRD Model—Based on ACPSO Optimization Algorithm
摘要: 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。
Abstract: 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.
文章引用：黄丽霞, 鲁延玲, 秦秀. 基于分阶段SEIAHRD模型的传染病预测分析——基于ACPSO优化算法[J]. 应用数学进展, 2023, 12(9): 3954-3967. https://doi.org/10.12677/AAM.2023.129387

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