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
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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
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Bayesian modelling of repeated cross-sectional epidemic prevalence survey data
Preprint
Steyn N. et al, (2025)
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Pandemic-risk-related behaviour change in England from June 2020 to March 2022: REACT-1 study among over 2 million people
Preprint
Steyn N. et al, (2025)
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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
Preprint
Steyn N. et al, (2025)
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Robust uncertainty quantification in popular estimators of the instantaneous reproduction number
Preprint
Steyn N. and Parag KV., (2024)