Metapopulations and infectious disease dynamics

复合种群和传染病动态

• 批准号: 2587459
• 金额: --
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2587459
• 关键词: Metapopulations; infectious; disease; dynamics

Studentship strategic priority area: Mathematical BiologyKeywords: Transmission, Lineages, Genomics, Inference, StatisticsAbstract: Background: Metapopulations are a fundamental concept in ecology whereby colonization and extinctions underpin the persistence of populations across landscapes. Metapopulation dynamics have important applications in conservation and in the control and elimination of infectious diseases. Genetic and epidemiological data from the SARS-CoV-2 pandemic perfectly illustrate this theory with the emergence of viral lineages and their frequent introductions and extinctions within interconnected populations around the world. Metapopulation dynamics have been hypothesized to underlie the endemic dynamics of canine rabies, with co-circulation of viral lineages and frequent reintroductions observed in a range of settings. We have shown how foci of rabies infection arise from individual variation in transmission and dispersal and how resulting lineages have characteristic patterns of persistence. Evidence from regional rabies elimination programmes also illustrate how metapopulation dynamics contribute to persistence and have implications for coordinated management. Aims of studentship: To parameterize a metapopulation model of rabies dynamics using individual level data on transmission and dispersal and genomic data on viral lineage diversity Apply this model across spatially heterogeneous landscapes to explore patterns of viral circulation. Compare model outcomes with genomic data from around the world using RABV-GLUE, a platform for curation of rabies viral sequences and metadata. Refine model to spatially explicit landscapes so as to investigate the optimal deployment of control efforts for elimination at national and regional scales. Examine how surveillance biases affect inference of disease dynamics and specifically how surveillance should be enhanced to improve disease elimination programmes. Training outcomes: Development and parameterization of mathematical models and experience in application of a range of modelling frameworks from individual-based to metapopulation models. Computational and statistical competence for undertaking computer simulations and statistical inference Understanding of viral sequencing methods and phylodynamics, with skills gained in the integration and synthesis of genomic and epidemiological data Understanding of routine and enhanced surveillance as part of national and regional rabies control programmes including viral sequencing Effective communication and data visualization skills for conveying information from mathematical models and genetic and epidemiological data and to inform other scientists, policy makers, practitioners and the general public

学生奖学金战略优先领域：数学生物学关键词：传输，血统，基因组学，推理，StatisticsAbstract：背景：集合种群是生态学中的一个基本概念，殖民化和保护是整个景观中种群持续存在的基础。集合种群动力学在保护和控制及消灭传染病方面有重要的应用。来自SARS-CoV-2大流行的遗传和流行病学数据完美地说明了这一理论，病毒谱系的出现及其在世界各地相互关联的人群中的频繁引入和传播。集合种群动态已被假设为犬狂犬病地方性动态的基础，在一系列环境中观察到病毒谱系的共循环和频繁的再引入。我们已经展示了狂犬病疫源地是如何从传播和扩散的个体变异中产生的，以及由此产生的谱系是如何具有持续存在的特征模式的。区域狂犬病消灭方案的证据也说明了集合种群动态如何有助于持久性，并对协调管理产生影响。学生奖学金的目的：使用有关传播和传播的个人水平数据以及有关病毒谱系多样性的基因组数据来参数化狂犬病动态的集合种群模型将该模型应用于空间异质景观，以探索病毒循环模式。 使用RABV-GLUE（狂犬病病毒序列和元数据管理平台）将模型结果与来自世界各地的基因组数据进行比较。 将模型细化到空间上明确的景观，以便调查在国家和区域尺度上消除控制工作的最佳部署。 研究监测偏差如何影响疾病动态的推断，特别是如何加强监测，以改善疾病消除计划。培训成果：数学模型的开发和参数化，以及从基于个体的模型到集合种群模型的一系列建模框架的应用经验。具有计算机模拟和统计推断的计算和统计能力，了解病毒测序方法和病毒动力学，掌握基因组和流行病学数据整合和综合方面的技能，了解作为国家和区域狂犬病控制计划一部分的常规和加强监测，包括病毒测序遗传学和流行病学数据，并向其他科学家、决策者、从业人员和公众提供信息