ATD Collaborative Research: A computational analysis of multi-strain structure in genetically diverse bacterial populations in a natural host environment

ATD 合作研究：自然宿主环境中遗传多样性细菌群体的多菌株结构的计算分析

• 批准号: 1021896
• 负责人: Kwang Woo Ahn
• 金额: $ 26.21万
• 依托单位: Medical College of Wisconsin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1021896&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; computational; analysis

The degree of bio-threat associated with newly detected pathogen variants (genotypes) that are genetically similar to some known pathogens may be assessed in terms of the cross-immunity between the two variants under study. Perfect (lack of) cross-immunity between the two variants suggests that the newly detected pathogen and the known variant are identical (distinct) epidemiologically. New epidemiological models will be developed for estimating cross-immunity in a two-strain system that may allow for variable birth rate of the natural hosts, possibility of vertical transmission and finite number of contacts per subject per unit time. Similar to many popular epidemiological models, the proposed epidemiological models stipulate that the dynamics of the state vector follow some nonlinear partial differential equation (PDE). New computationally efficient estimation methods are proposed for estimating a PDE model. The development of the proposed methodologies will be guided by analysis of a real monitoring longitudinal data on prevalence of various Bartonella variants (genotypes) in a natural population of rodents (cotton rats).The research team consists of two statisticians from two academic institutions and one epidemiologist from the CDC, who have worked closely together for a number of years. The proposed works will provide general tools for quantifying an epidemiological similarity between newly detected pathogen variant and known bacterial species, which contribute to the general problem on the assessment of bio-threat associated with newly detected variants. The proposed estimation methods can be generally applicable for estimating PDE models used in epidemiological studies, as well as in other fields, e.g. finance. A computer package implementing the proposed methods will be freely available to the public. The research team will continue to maintain the strong record of training PhD students in cross-disciplinary research.

与新检测到的与某些已知病原体在遗传上相似的病原体变体（基因型）相关的生物威胁程度可以根据所研究的两种变体之间的交叉免疫来评估。两种变种之间完美（缺乏）交叉免疫表明新检测到的病原体和已知变种在流行病学上是相同（不同）的。将开发新的流行病学模型，用于估计两种菌株系统中的交叉免疫，该模型可以考虑自然宿主的可变出生率、垂直传播的可能性以及每个受试者每单位时间的有限接触次数。与许多流行的流行病学模型类似，所提出的流行病学模型规定状态向量的动态遵循一些非线性偏微分方程（PDE）。提出了新的计算高效的估计方法来估计 PDE 模型。所提出的方法的开发将以对自然啮齿动物群体（棉鼠）中各种巴尔通体变体（基因型）患病率的真实监测纵向数据的分析为指导。研究团队由来自两个学术机构的两名统计学家和一名来自疾病预防控制中心的流行病学家组成，他们多年来一直密切合作。拟议的工作将为量化新检测到的病原体变体和已知细菌物种之间的流行病学相似性提供通用工具，这有助于解决与新检测到的变体相关的生物威胁评估的一般问题。所提出的估计方法通常适用于估计流行病学研究以及其他领域（例如，流行病学研究）中使用的 PDE 模型。金融。实施所提议方法的计算机软件包将免费向公众开放。研究团队将继续保持培养博士生进行跨学科研究的良好记录。