Theoretical Evolutionary Epidemiology

Ailene MacPherson博士的研究项目解决了进化，生态学和流行病学交叉的基本问题。长期目标是利用传染病来探索物种间的相互作用。在拟议的CRC任期内，MacPherson博士将解决两个短期目标：1）开发从病毒基因组数据中稳健估计传染病动态的方法; 2）分析数学模型并开发统计推断方法，以研究宿主和传染性病原体之间的共同进化，并促进有效的遗传保护策略的设计。麦克弗森研究团队将以多方面的方法解决这些研究目标。对于目标1，他们将开发和应用分析病毒基因组序列的方法，以告知疾病动态。具体来说，该团队将重点关注自然人群中特别重要的流行病学参数的估计以及创新公共卫生策略的设计，例如传染性疫苗的开发。目标2包括四个相辅相成的办法。首先，该团队将开发数学种群遗传模型，以对比共同进化适应的局部与全局模式。然后，他们将使用目标1的发现来扩展和综合物种相互作用对生物多样性影响的理论观点。在最后两种方法中，MacPherson博士和她的研究团队将从数学框架过渡到统计框架，利用寄生虫局部适应模式来识别参与物种相互作用的基因，并设计遗传保护策略，以帮助拯救面临灭绝风险的物种。拟议研究的结果将为加拿大人面临的存在主义科学挑战提供见解，从流行病控制到保护。

Dr. Ailene MacPherson's research program addresses fundamental questions at the intersection of evolution, ecology, and epidemiology. The long-term goal is to use infectious diseases to explore species interactions. During the proposed CRC term, Dr. MacPherson will address two short-term objectives: 1) Develop methods for robustly estimating infectious disease dynamics from viral genomic data and 2) Analyze mathematical models and develop statistical inference methods to examine coevolution between hosts and infectious pathogens and to facilitate the design of effective genetic conservation strategies. The MacPherson research team will tackle these research objectives with a multifaceted approach. For Objective 1 they will develop and apply methods for analyzing viral genome sequences to inform disease dynamics. Specifically, the team will focus on the estimation of epidemiological parameters of particular importance in natural populations and for the design of innovative public health strategies, such as the development of transmissible vaccines. Objective 2 consists of four complementary approaches. First, the team will develop mathematical population genetic models to contrast local versus global patterns of coevolutionary adaptation. Then they will use the findings from Objective 1 to expand and synthesize theoretical perspectives on the effect of species interactions on biological diversity. In the final two approaches, Dr. MacPherson and her research team will transition from a mathematical to a statistical framework, using patterns of parasite local adaptation to identify genes involved in species interactions and to design genetic conservation strategies to help rescue species at risk of extinction from infectious disease. The outcomes of the proposed research will provide insights on existential scientific challenges faced by Canadians, from pandemic control to conservation.