Statistical​ ​Methods​ ​for​ Challenging​ Problems​ ​in​ Public​ Health​ Microbiology

Why Study Public Health Microbiology?

Pathogenic microbial organisms cause a significant burden of disease, especially in hospital settings. Drug resistance, whereby a pathogen no longer responds to a drug treatment, is particularly relevant today. 

Pathogen outbreaks require the development of surveillance tools to rapidly track and disrupt the chain of transmissions. Making fast, reliable and affordable WGS methods available to health authorities has the potential for major benefit. 

WGS methods are still in their infancy. In order to fully harness their power, novel statistical and algorithmic techniques for microbial WGS data must be developed. 

Areas of Exploration

Variant-Calling Methodology

Includes developing a likelihood-based method for calling genomic variants (e.g. SNPs, insertions, deletions or alleles of specific genes) informed by a microbial evolutionary model.

Algorithms for WGS Data Analysis

Includes training a statistical or machine learning algorithm to combine multiple signals into a single call. This is done to predict drug resistance, phylogenetic relatedness or epidemiological relatedness directly from WGS data.

Estimating Sample Size and Power

Includes designing methods for estimating the power of studies for detecting regular genotype-phenotype associations in bacteria, as well as epistatic interactions. These interactions are known to require prohibitively high sample sizes in human genetics.

Solving Global Challenges

Research Team’s Goal

To tackle three unsolved statistical challenges that arise in public health microbiology, and deploy the developed methods in a publicly available computational platform.