Through its long-standing partnership with Public Health England (PHE), operationalized by the team of Professor Daniela De Angelis, the MRC Biostatistics Unit (BSU) is involved in the modelling work of the current COVID-19 epidemic that underlies the government’s decisions. Together with groups,  including Imperial College, LSHTM, Warwick, Manchester, Exeter, Lancaster, the MRC BSU (Daniela De Angelis) is part of SPIM (Scientific Pandemic Influenza Group on Modelling) which provides real time information to government through SAGE (Scientific Advisory Group for Emergencies).

The MRC BSU is now focusing a large part of its research effort on leading and contributing to a number of COVID-19 analyses, involving anonymised health data collected at different levels of granularity (national or local).

The government has recently published the scientific evidence behind the COVID-19 strategy:

• Press release: https://www.gov.uk/government/news/coronavirus-covid-19-scientific-evidence-supporting-the-uk-government-response
• Scientific reports: https://www.gov.uk/government/groups/scientific-advisory-group-for-emergencies-sage-coronavirus-covid-19-response

Key publications which underpin the statistical approaches which are currently adapted and implemented in the BSU to analyse the UK data for the COVID 19 epidemic.

• Birrell, P.J., Ketsetzis, G., Gay, N.J., Cooper, B.S., Presanis, A.M., Harris, R.J., Charlett, A., Zhang, X.S., White, P.J., Pebody, R.G. and De Angelis, D., 2011. Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. Proceedings of the National Academy of Sciences, 108(45), pp.18238-18243.
• Presanis, A.M., Pebody, R.G., Paterson, B.J., Tom, B.D.M., Birrell, P.J., Charlett, A., Lipsitch, M. and De Angelis, D., 2011. Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis. Bmj, 343, p.d5408.