Price Ian, Mochan-Keef Ericka D, Swigon David, Ermentrout G Bard, Lukens Sarah, Toapanta Franklin R, Ross Ted M, Clermont Gilles
Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA.
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
J Theor Biol. 2015 Jun 7;374:83-93. doi: 10.1016/j.jtbi.2015.03.017. Epub 2015 Apr 3.
Mortality from influenza infections continues as a global public health issue, with the host inflammatory response contributing to fatalities related to the primary infection. Based on Ordinary Differential Equation (ODE) formalism, a computational model was developed for the in-host response to influenza A virus, merging inflammatory, innate, adaptive and humoral responses to virus and linking severity of infection, the inflammatory response, and mortality. The model was calibrated using dense cytokine and cell data from adult BALB/c mice infected with the H1N1 influenza strain A/PR/8/34 in sublethal and lethal doses. Uncertainty in model parameters and disease mechanisms was quantified using Bayesian inference and ensemble model methodology that generates probabilistic predictions of survival, defined as viral clearance and recovery of the respiratory epithelium. The ensemble recovers the expected relationship between magnitude of viral exposure and the duration of survival, and suggests mechanisms primarily responsible for survival, which could guide the development of immuno-modulatory interventions as adjuncts to current anti-viral treatments. The model is employed to extrapolate from available data survival curves for the population and their dependence on initial viral aliquot. In addition, the model allows us to illustrate the positive effect of controlled inflammation on influenza survival.
流感感染导致的死亡率仍是一个全球公共卫生问题,宿主的炎症反应会导致与原发性感染相关的死亡。基于常微分方程(ODE)形式,开发了一个计算模型,用于模拟甲型流感病毒在宿主体内的反应,该模型融合了对病毒的炎症、先天、适应性和体液反应,并将感染的严重程度、炎症反应和死亡率联系起来。该模型使用来自感染亚致死剂量和致死剂量H1N1流感毒株A/PR/8/34的成年BALB/c小鼠的密集细胞因子和细胞数据进行校准。使用贝叶斯推理和集成模型方法对模型参数和疾病机制的不确定性进行量化,该方法生成生存概率预测,定义为病毒清除和呼吸道上皮恢复。该集成模型恢复了病毒暴露量与生存持续时间之间的预期关系,并提出了主要负责生存的机制,这可以指导免疫调节干预措施的开发,作为当前抗病毒治疗的辅助手段。该模型用于根据现有数据推断人群的生存曲线及其对初始病毒样本量的依赖性。此外,该模型使我们能够说明控制炎症对流感生存的积极影响。