Nicholas Steyn
DPhil Student StatML
I am a second year DPhil student studying on the Modern Statistics and Statistical Machine Learning CDT in the Department of Statistics. Following the completion of my undergraduate studies in my home-city of Christchurch, New Zealand, my research career was born within the COVID-19 pandemic.
Between March 2020 and September 2021 I worked in a government-funded modelling programme, applying mathematical and statistical methods to directly inform New Zealand’s COVID-19 policy. It was this experience – using statistical methods in real time to inform policy – that lead me to pursue a DPhil.
I have an undergraduate degree in Statistics and Financial Engineering from the University of Canterbury (2018), and a first-class honour’s degree in Applied Mathematics (2019) from the same university. In addition to being enrolled at University College, Oxford, I am also an affiliate student at Imperial College London.
My research interests are in statistical methodology for epidemiology, the science-policy interface, and real-time outbreak analysis of infectious diseases.
Recent publications
Robust uncertainty quantification in popular estimators of the instantaneous reproduction number
Journal article
Steyn N. and Parag KV., (2025), American Journal of Epidemiology, 194, 3355 - 3363
Regional and national estimates of children affected by all-cause and COVID-19-associated orphanhood and caregiver death in Brazil, by age and family circumstance: a modeling study
Journal article
Steyn N. et al, (2025), The Lancet Regional Health - Americas, 51, 101252 - 101252
A Bayesian model for repeated cross-sectional epidemic prevalence survey data
Journal article
Steyn N. et al, (2025), PLOS Computational Biology, 21, e1013515 - e1013515
A Primer on Inference and Prediction With Epidemic Renewal Models and Sequential Monte Carlo
Journal article
Steyn N. et al, (2025), Statistics in Medicine, 44
A Bayesian model for repeated cross-sectional epidemic prevalence survey data
Preprint
Steyn N. et al, (2025)