Parameter Estimation for a Class of Fractional Stochastic SIRD Models with Random Perturbations

Na NİE, Jun JİANG, Yuqiang FENG
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Abstract

The classical SIRD model is extended to the conformable fractional stochastic SIRD model. The differences between the fractional stochastic SIRD model and the integer stochastic SIRD model are analyzed and compared using COVID-19 data from India. The results show that when the order of the fractional stochastic SIRD model is between $[0.93,0.99]$, the root mean square error between the simulated value and the real value of the number of infections is smaller than that of the integer stochastic SIRD model. Then, the maximum likelihood estimation of the parameters of the conformable fractional stochastic SIRD model is carried out, and compared with the maximum likelihood estimation results of the parameters of the integer stochastic SIRD model, It can be seen that the root mean square error of the fractional stochastic SIRD model is smaller when the fractional order is between $[0.93,0.99]$.
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一类具有随机扰动的分数阶随机SIRD模型的参数估计
将经典SIRD模型推广为符合分数阶随机SIRD模型。利用印度的COVID-19数据,分析比较了分数型随机SIRD模型与整数型随机SIRD模型的差异。结果表明,当分数阶随机SIRD模型阶数在$[0.93,0.99]$之间时,感染数模拟值与真实值的均方根误差小于整数阶随机SIRD模型的均方根误差。然后,对合规性分数阶随机SIRD模型的参数进行极大似然估计,与整数阶随机SIRD模型参数的极大似然估计结果相比,可以看出分数阶随机SIRD模型在$[0.93,0.99]$之间时均方根误差更小。
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