Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.
Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.
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DOI：
10.1007/s11071-023-08308-x
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发表时间：
2023
影响因子：
5.6
通讯作者：
Gao, Bo
中科院分区：
文献类型：
--
作者：
Dong, Suyalatu;Xu, Linlin;Yana, A.;Lan, Zhong-Zhou;Xiao, Ding;Gao, Bo
在经典的传染病房室模型中，参数是固定的。现实中，疾病传播过程中病毒传播的概率取决于环境中病毒的浓度，而浓度又取决于环境中患者的比例。因此，病毒传播的概率会随着时间而变化。然后如何对模型中的参数进行拟合，得到参数随时间的变化趋势，是利用模型预测疾病进程的关键。本文基于校准期间美国COVID-19疫情统计数据，利用机器科学的线性回归算法，对感染率、治愈率等参数进行拟合，得出这些参数随时间变化的规律。然后提出了具有时滞和疫苗接种的SIR模型，并利用最优控制理论和庞特里亚金极大值原理分析了疫情的最优控制策略，证明了该控制策略在抑制COVID-19传播方面的有效性。数值模拟结果表明，我们的模型得到的活跃病例数的时变规律基本符合校准期内美国COVID-19疫情统计数据的真实的变化规律。此外，我们预测了校准周期以外的未来美国COVID-19疫情活跃病例数随时间的变化，预测结果更符合实际疫情数据。
In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.
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影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
7.8
作者：
Sarkar, Kankan;Khajanchi, Subhas;Nieto, Juan J.
通讯作者：
Nieto, Juan J.
影响因子：
5
作者：
Tchoumi SY;Diagne ML;Rwezaura H;Tchuenche JM
通讯作者：
Tchuenche JM
DOI：
10.1016/j.chaos.2021.110682
发表时间：
2021-03
期刊：
Chaos, solitons, and fractals
影响因子：
--
作者：
Muñoz-Fernández GA;Seoane JM;Seoane-Sepúlveda JB
通讯作者：
Seoane-Sepúlveda JB
影响因子：
4.1
作者：
Rihan, F. A.;Alsakaji, H. J.;Rajivganthi, C.
通讯作者：
Rajivganthi, C.