Amiri Mehra Amir Hossein, Shafieirad Mohsen, Abbasi Zohreh, Zamani Iman
Department of Electrical and Computer Engineering, University of Kashan, Kashan, Iran.
Electrical and Electronic Engineering Department, Shahed University, Tehran, Iran.
Comput Math Methods Med. 2020 Dec 27;2020:1465923. doi: 10.1155/2020/1465923. eCollection 2020.
In this paper, the SIR epidemiological model for the COVID-19 with unknown parameters is considered in the first strategy. Three curves (, , and ) are fitted to the real data of South Korea, based on a detailed analysis of the actual data of South Korea, taken from the Korea Disease Control and Prevention Agency (KDCA). Using the least square method and minimizing the error between the fitted curve and the actual data, unknown parameters, like the transmission rate, recovery rate, and mortality rate, are estimated. The goodness of fit model is investigated with two criteria (SSE and RMSE), and the uncertainty range of the estimated parameters is also presented. Also, using the obtained determined model, the possible ending time and the turning point of the COVID-19 outbreak in the United States are predicted. Due to the lack of treatment and vaccine, in the next strategy, a new group called quarantined people is added to the proposed model. Also, a hidden state, including asymptomatic individuals, which is very common in COVID-19, is considered to make the model more realistic and closer to the real world. Then, the SIR model is developed into the SQAIR model. The delay in the recovery of the infected person is also considered as an unknown parameter. Like the previous steps, the possible ending time and the turning point in the United States are predicted. The model obtained in each strategy for South Korea is compared with the actual data from KDCA to prove the accuracy of the estimation of the parameters.
在本文的第一种策略中，考虑了参数未知的COVID-19的SIR流行病学模型。基于对韩国疾病控制与预防机构（KDCA）提供的韩国实际数据的详细分析，将三条曲线（S、I和R）拟合到韩国的实际数据。使用最小二乘法并最小化拟合曲线与实际数据之间的误差，估计诸如传播率、康复率和死亡率等未知参数。用两个标准（SSE和RMSE）研究拟合模型的优度，并给出估计参数的不确定范围。此外，使用得到的确定模型，预测了美国COVID-19疫情的可能结束时间和转折点。由于缺乏治疗方法和疫苗，在接下来的策略中，在所提出的模型中增加了一个名为被隔离者的新群体。此外，考虑了一个隐藏状态，包括无症状感染者，这在COVID-19中非常常见，以使模型更现实、更贴近现实世界。然后，将SIR模型发展为SQAIR模型。感染者康复的延迟也被视为一个未知参数。与之前的步骤一样，预测了美国的可能结束时间和转折点。将韩国在每种策略中获得的模型与KDCA的实际数据进行比较，以证明参数估计的准确性。
