Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections

The slowly decaying viral dynamics, even after 2–3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.即便在确诊2~3周后，新冠病毒（COVID-19）感染仍呈现病毒动力学缓慢衰减的特征，这类特征在当前理论与实验研究中仍未得到充分探索。在细胞层面固有变异与噪声的背景下，病毒感染的这种长时存续特征往往被忽视。为此，本研究通过构建随机非马尔可夫（stochastic non-Markovian）模型，旨在解析此类变异的影响；该模型不仅能够刻画免疫细胞与病毒之间的耦合动力学过程，还可用于探究波动效应。模型的数值模拟结果表明，波动中的长程时间相关性主导了病毒感染的长时存续动力学过程，进而还会影响免疫应答的速率。此外，本模型系统的预测结果与多国新冠患者的病毒载量实验数据吻合良好。