Department of Mathematics, Virginia Polytechnic Institute and State University, 225 Stanger Street, Blacksburg, VA, 24060, USA.
Department of Mathematics, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, 33431, USA.
Sci Rep. 2022 Aug 27;12(1):14637. doi: 10.1038/s41598-022-18683-x.
Determining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of the parameter estimates to the noise in the data, we developed a novel two-patch within-host mathematical model that considered the infection of both respiratory tracts and assumed that the viral load in the lower respiratory tract decays in a density dependent manner and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.
通过拟合数学模型来确定上呼吸道和下呼吸道中严重急性呼吸综合征冠状病毒 2 的特征的准确估计值是困难的，因为在感染早期缺乏测量数据。为了确定参数估计值对数据噪声的敏感性，我们开发了一种新颖的两斑块宿主内数学模型，该模型考虑了两个呼吸道的感染，并假设下呼吸道中的病毒载量以密度依赖的方式衰减，并研究了其匹配人群水平数据的能力。我们提出了几种可以提高参数实际可识别性的方法，包括一种最佳实验方法，并发现感染早期病毒数据的可用性对于提高估计值的准确性至关重要。我们的研究结果对于设计干预措施可能是有用的。
