Mathematical and computational approaches for viral infection

该项目在数学病毒学和数学免疫学领域，其基础假设是，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，为支持有关医疗策略的关键决策提供证据。该项目的主要目标是开发EBOV和治疗性干扰粒子（TIPs）共同进化的新模型，并将这些种群水平的模型与宿主内模型以及Dstl生成的实验数据联系起来。该项目的目标是在现有的野生型EBOV模型的基础上，为TIPs和野生型EBOV的种群水平传播建立新的数学模型。这些模型的目的将是研究WT EBOV和TIPs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，个体之间的传播率作为宿主内病毒载量的函数，个体的恢复率取决于宿主内免疫状态，（或以生存/死亡作为病毒载量的函数的个体临床结果），-确定适当的汇总统计（随机描述符），以评估TIPs控制疾病传播的有效性（例如，繁殖数量、爆发规模），-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综合策略的效果。并且-使用新开发的随机种群水平模型的预测来告知dstl的科学家关于感染的最小多重性（例如，就TIP与WT的竞争优势而言），使tips成为有效的疾病传播控制措施。研究项目的新颖性该项目在数学上的新颖性和挑战在于将分子、细胞和群体尺度结合起来，以了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、随机描述子理论[1]、贝叶斯推理和基于agent的建模，结合Dstl的实验数据，预测WTEBOV和TIPs的期望感染次数，使TIPs成为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些随机描述符，并研究病毒灭绝的概率和时间。学生亦将利用新颖的贝叶斯统计方法，将Dstl的实验数据与本计划所建立的宿主内病毒感染的数学模型结合起来，以进行参数推断。(1)项目的潜在应用Dstl最近获得了DARPA的资助，用于开发基于治疗性干扰粒子（TIPs）的埃博拉病毒的新型医学治疗方法。该项目产生的模型将支持设计联合感染传播控制措施，其中可能包括使用TIPs和其他现有的EBOV策略，使研究组织、公共卫生和医疗机构受益。Dstl还将使用这些模型向英国政府提供有关国防、安全和公共卫生情况下EBOV治疗的建议。最后，将利用政府与国际合作伙伴共享信息的机制，使该项目的产出对与公共卫生、国防和安全有关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.