Photograph of Seth

I am working with colleagues from Imperial’s Department of Mathematics and School of Public Health to model the spread of COVID-19.

Peer reviewed COVID-19 research:
“Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe”, Flaxman, Mishra, Gandy et al, Nature, 2020 with accompanying R package.

“Age groups that sustain resurging COVID-19 epidemics in the United States” Monod et al, Science, 2021.

“State-level tracking of COVID-19 in the United States” Unwin et al, Nature Communications, 2020.

“Inference of COVID-19 epidemiological distributions from Brazilian hospital data” Hawryluk et al, 2020.

“Have deaths from COVID-19 in Europe plateaued due to herd immunity?” Okell et al, Lancet, 2020.

Local area UK estimates of cases and the reproduction number R: updated daily.

News coverage of research: click here.

I am a senior lecturer in statistical machine learning in the statistics section of the Department of Mathematics at Imperial College London. I help lead the Machine Learning Initiative at Imperial and the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning (StatML) at Imperial and Oxford. My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science. I’ve worked on application areas that include public health, crime, voting patterns, filter bubbles / echo chambers in media, the regulation of machine learning algorithms, and emotion.

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