COVID-19 research: 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: "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe", Flaxman, Mishra, Gandy et al, Nature, 2020 with accompanying website: https://mrc-ide.github.io/covid19estimates/
Reports (under review):
- USA: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-23-united-states/ and website: https://mrc-ide.github.io/covid19usa/
- Brazil: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-21-brazil/
- Italy: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-20-italy/
Up to date news coverage of my research can be found here.
I am a senior lecturer in the statistics section of the Department of Mathematics at Imperial College London. I help lead the Machine Learning Initiative at Imperial and the StatML CDT (Imperial/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. Find more information on my website.
et al., 2020, Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'., Nature, Vol:584, ISSN:0028-0836, Pages:425-429
et al., 2020, Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study, Lancet Infectious Diseases, ISSN:1473-3099
et al., 2020, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, Vol:584, ISSN:0028-0836, Pages:257-261