Dominic Arthur

My PhD looks at superspreading events during the COVID-19 pandemic in Scotland. I use genomic data and modelling to understand how outbreaks unfold and why some people or settings drive more transmission than others. Beyond research, I’m deeply interested in the intersection of faith and science, particularly how scientific theories relate to reality and shape society’s understanding of truth.

I am a PhD researcher studying superspreading events during the COVID-19 pandemic in Scotland, combining genomic data and infectious disease modelling to understand how outbreaks unfold and why certain individuals or settings drive greater transmission. I work extensively with large-scale datasets (over 350,000 genomes) and apply Python-based analytical and statistical approaches to investigate transmission dynamics.

Previously, I served as a demonstrator in undergraduate data science and Python programming courses, supporting students in developing their analytical and coding skills. I also worked as a research assistant on a genome-wide association study (GWAS) exploring the genetics of social support, where I contributed to data cleaning, analysis, and interpretation.

My research is informed by a deep interest in the philosophy of science, particularly in how scientific theories connect to reality and shape society’s understanding of truth and knowledge.

I am currently pursuing a PhD in Epidemiology focused on COVID-19 superspreading events, integrating genomic, epidemiological, and statistical approaches to study transmission heterogeneity.

My academic background bridges data science, genomics, and public health, providing a strong foundation in quantitative analysis, infectious disease dynamics, and computational research methods.