School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
PLoS Comput Biol. 2024 Jul 23;20(7):e1012264. doi: 10.1371/journal.pcbi.1012264. eCollection 2024 Jul.
The role of direct cell-to-cell spread in viral infections-where virions spread between host and susceptible cells without needing to be secreted into the extracellular environment-has come to be understood as essential to the dynamics of medically significant viruses like hepatitis C and influenza. Recent work in both the experimental and mathematical modelling literature has attempted to quantify the prevalence of cell-to-cell infection compared to the conventional free virus route using a variety of methods and experimental data. However, estimates are subject to significant uncertainty and moreover rely on data collected by inhibiting one mode of infection by either chemical or physical factors, which may influence the other mode of infection to an extent which is difficult to quantify. In this work, we conduct a simulation-estimation study to probe the practical identifiability of the proportion of cell-to-cell infection, using two standard mathematical models and synthetic data that would likely be realistic to obtain in the laboratory. We show that this quantity cannot be estimated using non-spatial data alone, and that the collection of data which describes the spatial structure of the infection is necessary to infer the proportion of cell-to-cell infection. Our results provide guidance for the design of relevant experiments and mathematical tools for accurately inferring the prevalence of cell-to-cell infection in in vitro and in vivo contexts.
直接细胞间传播在病毒感染中的作用——病毒颗粒在宿主和易感细胞之间传播，而无需分泌到细胞外环境中——已被认为是丙型肝炎和流感等具有医学意义的病毒动力学的关键。实验和数学模型文献中的最近研究试图使用各种方法和实验数据来量化细胞间感染相对于传统游离病毒途径的流行程度。然而，估计值存在很大的不确定性，而且依赖于通过化学或物理因素抑制一种感染模式收集的数据，这可能会在难以量化的程度上影响另一种感染模式。在这项工作中，我们进行了一项模拟估计研究，使用两个标准的数学模型和可能在实验室中获得的合成数据来探究细胞间感染比例的实际可识别性。我们表明，仅使用非空间数据无法估计该数量，并且需要收集描述感染空间结构的数据才能推断细胞间感染的比例。我们的结果为设计相关实验和数学工具提供了指导，以准确推断体外和体内环境中细胞间感染的流行程度。
