Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4370-4373. doi: 10.1109/EMBC46164.2021.9630629.
SARS-CoV-2 has emerged to cause the outbreak of COVID-19, which has expanded into a worldwide human pandemic. Although detailed experimental data on animal experiments would provide insight into drug efficacy, the scientists involved in these experiments would be exposed to severe risks. In this context, we propose a computational framework for studying infection dynamics that can be used to capture the growth rate of viral replication and lung epithelial cell in presence of SARS-CoV-2. Specifically, we formulate the model consisting of a system of non-linear ODEs that can be used for visualizing the infection dynamics in a cell population considering the role of T cells and Macrophages. The major contribution of the proposed simulation method is to utilize the infection progression model in testing the efficacy of the drugs having various mechanisms and analyzing the effect of time of drug administration on virus clearance.Clinical Relevance-The proposed computational framework incorporates viral infection dynamics and role of immune response in Covid-19 that can be used to test the impact of drug efficacy and time of drug administration on infection mitigation.
SARS-CoV-2 的出现引发了 COVID-19 疫情，这场疫情已经蔓延到全球范围。尽管动物实验的详细实验数据可以为药物疗效提供深入了解，但参与这些实验的科学家将面临严重的风险。在这种情况下，我们提出了一种用于研究感染动力学的计算框架，可以用来捕捉病毒复制和肺上皮细胞在 SARS-CoV-2 存在下的增长率。具体来说，我们构建了一个由非线性 ODE 系统组成的模型，该模型可以用于考虑 T 细胞和巨噬细胞的作用，可视化细胞群体中的感染动力学。所提出的模拟方法的主要贡献在于利用感染进展模型来测试具有不同机制的药物的疗效，并分析药物给药时间对病毒清除的影响。临床相关性——所提出的计算框架结合了病毒感染动力学和免疫反应在 COVID-19 中的作用，可以用于测试药物疗效和给药时间对感染缓解的影响。
