Statistical Methods for Integrating epidemiological and whole genome sequence data for effectively analysing infectious disease outbreak data

在过去几年中，测序技术的进步和相关成本的降低使科学家能够以前所未有的规模获得有关致病病原体的非常详细的基因组数据。除了这些数据中包含固有的系统发育信息外，将基因组数据与传统的流行病学数据（如病例发病率时间序列）相结合也提供了执行微生物来源归属的机会，即确定病原体通过人群的实际传播途径。尽管最近取得了进展，但现有方法有其自身的局限性，可能会产生估计偏差并导致误导性结果。该项目涉及：i)通过扩展Worby等人（2016）的方法，开发模型和计算效率高的方法，以有效分析流行病学和高分辨率遗传数据。ii)应用于方法的实际数据。

In the past few years, advances in sequencing technology and the reduction in associated costs have enabled scientists to obtain highly detailed genomic data on disease-causing pathogens on a scale never seen before. In addition to the inherent phylogenetic information contained in such data, combining genomic data with traditional epidemiological data (such as time series of case incidence) also provides an opportunity to perform microbial source attribution, i.e. determining the actual transmission pathway of the pathogen through a population.Despite the recent advances, existing approaches have their own limitations which can create estimation biases and lead to misleading results.This project is concerned with: i) developing models and computationally efficient methods to effectively analyse epidemiological and high-resolution genetic data by extending the approach of Worby et al.(2016) ii) apply to methods real-data.