Mathematical and computational approaches for viral infection

该项目在数学病毒学和数学免疫学领域，基于以下假设：数学模型有可能提供动物实验的替代方案，并且可能具有足够的预测性，以提供证据支持有关医疗策略的关键决策。该项目的主要目的是开发EBOV和治疗性干扰粒子（TIP）共同进化的新模型，并将这些群体水平模型与宿主内模型以及Dstl生成的实验数据联系起来。该项目的目标是-开发新的数学模型的人口水平的传播TIPs和WT EBOV，构建这些新的模型的基础上，现有的WT EBOV模型。这些模型的目的将是研究WT EBOV和TIP的共同进化和共同传播，-使用关于宿主内动态的预测和数据来告知群体水平模型（例如，作为宿主内病毒载量的函数的个体间的传播率，取决于宿主内免疫状态的个体的恢复率，或作为病毒载量的函数的存活/死亡方面的个体临床结果），-鉴定用于评估TIP对于疾病传播控制的功效的适当的概括统计（随机描述符）（例如，繁殖数量，爆发的规模），-将这种疾病传播控制措施与现有的替代措施进行比较，并利用流行病学模型分析组合策略的效果，以及-使用新开发的随机人口水平模型的预测，向Dstl的科学家通报感染的最小多重性（例如，研究项目的新奇该项目的数学新颖性和挑战性是将分子、细胞和群体尺度结合在一起来理解病毒和感染动力学。学生将利用广义的出生和死亡马尔可夫过程，随机描述符理论[1]，贝叶斯推理和基于代理的建模，以便与Dstl的实验数据一起，他可以预测WTEBOV和TIPs的预期感染复数，使TIPs成为一种有效的疾病传播控制措施。学生将利用新颖的矩阵分析方法来研究和分析一种随机描述符的数量，并研究病毒灭绝的概率和时间。学生还将利用新颖的贝叶斯统计方法，将Dstl产生的实验数据与项目中开发的宿主内病毒感染的数学模型结合起来，以进行参数推断。他还将开发新的基于代理的模型来研究感染动力学（I）Dstl项目的潜在应用最近收到了DARPA的资助，以开发基于治疗干扰颗粒（TIP）的埃博拉病毒新的医学治疗方法。该项目产生的模型将支持设计综合感染传播控制措施，其中可能包括使用TIP和其他现有的EBOV策略，使研究机构、公共卫生和医疗机构受益。这些模型也将被Dstl用于为英国政府提供关于在国防、安全和公共卫生相关场景中治疗EBOV的建议。最后，将利用政府与国际合作伙伴分享信息的机制，使该项目的成果能够对公共卫生、国防和安全方面的决策和研究产生国际影响。

The project, in the fields of Mathematical Virology and Mathematical Immunology, is based on the hypothesisthat mathematical models have potential to provide an alternative to animal experiments, and may be sufficiently predictive to provide evidence to support crucial decisions regarding medical treatment strategies. The main aim of the project is to develop new models for the co-evolution of EBOV and therapeutic interfering particles (TIPs) and to link these population-level models with within-host models, and with the experimental data generated by Dstl. The objectives of the project are- to develop new mathematical models for the population-level transmission of TIPs and WT EBOV, constructingthese new models based on existing WT EBOV models. The aim of these models will be to studythe co-evolution and co-transmission of WT EBOV and TIPs,- to use predictions and data regarding within-host dynamics for informing population-level models (e.g.,transmission rate among individuals as a function of within-host viral load, recovery rate of individualsdepending on the within-host immune status, or individual clinical outcome in terms of survival/death as afunction of viral load),- to identify appropriate summary statistics (stochastic descriptors) for assessing the efficacy of TIPs fordisease propagation control (e.g., reproduction number, size of the outbreak),- to compare this disease propagation control measure with alternative existing ones, and to analyse, makinguse of the epidemiological model, the effect of combined strategies, and- to use predictions from the newly-developed stochastic population-level models to inform scientists at Dstlabout the minimum multiplicity of infection (e.g., in terms of TIP competitive advantage with the WT) forTIPs to become an efficient disease propagation control measure.Novelty of the research projectThe mathematical novelty and challenge of the project is to bring together the molecular, cellular and populationscales to understand virus and infection kinetics. The student will make use of generalised birth and deathMarkov processes, the theory of stochastic descriptors [1], Bayesian inference, and agent-based modelling, sothat together with the experimental data from Dstl, he can predict the desired multiplicity of infection of WTEBOV and TIPs, for TIPs to become an efficient disease propagation control measure.The student will make use of novel matrix analytic methods to study and analyse a number of stochasticdescriptors and to study probability and times to viral extinction. The student will also make use of novelBayesian statistical methods to bring together experimental data generated at Dstl with the mathematical modelsof within-host viral infection developed in the project in order to carry out parameter inference. He will alsodevelop novel agent-based models to characterise infection kinetics(I) Potential applications of the project Dstl has recently received DARPA funding to develop novel medical treatments for Ebola virus, based on the use of therapeutic interfering particles (TIPs). The models generated in this project will support the design of combined infection spread control measures, which could include the use of TIPs and other existing strategiesfor EBOV, benefiting research organisations, public health and medical institutions. These models will alsobe used by Dstl to provide UK Government with advice about the treatment of EBOV in scenarios relevant toDefence, Security and Public Health. Finally, government mechanisms for sharing information with internationalpartners will be exploited to enable the outputs of this project to have international impact on decision-makingand research related to Public Health, Defence and Security.