Disentangling viral respiratory infections and coinfection: a modelling approach that links population surveillance and experimental data

学生战略重点领域：人口健康科学与公共卫生关键词：呼吸道病毒、建模、病毒-病毒相互作用、定量分析多种病毒导致呼吸道感染和合并感染，导致一系列疾病，从轻度感冒到危及生命的肺炎。呼吸道病毒通常作为单个实体而不是作为一个群体来研究。然而，有证据表明，病毒之间存在相互作用，这对患者健康和种群规模的感染动态很重要。数学模型是控制传染病的重要工具，在人口规模上得到了广泛的应用。然而，只有少数研究集中在个体宿主规模上，对共感染的关注就更少了。该项目将使用来自实验感染和共感染的数据来模拟病毒-病毒相互作用。我们将结合定量数据的病毒进入，病毒复制，病毒传播，先天免疫动力学和细胞死亡和再生。这项工作的结果将确定直接（病毒-病毒）和间接（病毒-免疫反应）相互作用在共感染中的相对作用，以改善患者护理。

Studentship strategic priority area:Population Health Sciences and Public HealthKeywords: Respiratory viruses, modelling, virus-virus interactions, quantitative analysisMultiple viruses are responsible for respiratory infections and co-infections that causing a range of disease, from a mild cold to life-threatening pneumonia. Respiratory viruses are generally studied as single entities and not as a community. However, evidence suggest that there are interactions between viruses that are important for patient health and for infection dynamics at the population scale. Mathematical models are essential tools for the control of infectious diseases and have been used extensively at the population scale. However, only a handful of studies have focused on the individual host scale and even less attention has been paid to coinfections. This project will use data from experimental infections and coinfections to model virus-virus interactions. We will combine quantitative data on virus entry, virus replication, virus spread, innate immune kinetics and cellular death and regeneration. Results from this work will identify the relative roles of direct (virus-virus) and indirect (virus-immune response) interactions in coinfections with the goal of improving patient care.