29325Indian Institute of Management Kozhikode, India.
Indian Institute of Management Visakhapatnam, India.
Stat Methods Med Res. 2022 Nov;31(11):2137-2163. doi: 10.1177/09622802221115877. Epub 2022 Aug 17.
In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (usceptible, nfected and pre-symptomatic, Infected and Symptomatic but ot Tested, ested Positive, Recorded ecovered, nrecorded Recovered, and xpired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.
在本文中，我们开发了一种适合 COVID-19 的房室流行病学模型的扩展。本文提出的模型包含疾病进展的七个隔间。该模型名为 SINTRUE（易感、感染和无症状、感染和有症状但未经检测、检测阳性、记录康复、未记录康复和死亡）模型。该模型纳入了无症状携带者的传播，并捕获了由于人们在国内不同行政边界之间的流动而导致的疾病传播。此外，该模型还可以估计人群中未记录的感染数量和未记录的康复数量。模型中的相关参数有助于制定公共卫生政策和大流行的运营管理。结果表明，无症状患者的检测率是对抗大流行的关键参数。该模型也被证明比其他流行病学模型具有更好的预测能力。
