Rosario is a Health Data Science DPhil candidate supervised by Dr Katrina Lythgoe and Dr Lei Clifton, investigating within-host viral diversity and evolution through genomic data analysis and applied machine learning methods. Her research utilises data from the Office for National Statistics Covid-19 Infection Survey (over 120,000 genomes) to examine short-read sequencing artefacts and study infection trajectories. By analysing patterns of genetic diversity within individual hosts, her work aims to understand how this diversity drives viral evolution at the population level, ultimately contributing to pandemic preparedness efforts.
Prior to this, Rosario graduated with an MBiol in Biological Sciences from the University of Oxford. During her undergraduate studies, she undertook two summer research internships with the Kraemer Lab, focusing on computational and spatial infectious disease modelling. Her Master's thesis assessed the impact of human mobility patterns on the reproduction number of SARS-CoV-2 in England. Following this, she completed a year of data science and statistical training through the Health Data Science CDT, funded by UKRI EPSRC, which also funds her DPhil.
