The Turing-RSS Health Data Lab

A working partnership between The Alan Turing Institute and Royal Statistical Society, providing independent research and support to the UK Health Security Agency

Project status



The Turing-RSS Health Data Lab is a working partnership between The Alan Turing Institute and Royal Statistical Society (RSS) supporting the UK Health Security Agency (UKHSA).

We provide an independent source of statistical modelling and machine learning expertise to address policy-relevant research questions.

Established in August 2020, The Turing-RSS Health Data Lab has focused on conducting world leading statistical research to meet the needs of current and future health surveillance systems.

Projects to date have included investigating social inequalities in COVID-19 risk, methods for de-biasing routine testing data, transmission and mobility modelling, the use of wastewater as a biomarker for local prevalence, COVID-19 genomics, and a rigorous assessment of a biomedical acoustic marker as a COVID-19 diagnostic.

This collaboration bridges the gap between rapid-response analysis and longer-term research projects whilst establishing an innovative interoperable modelling approach that allows our methods and algorithms to be transferable, sustainable and re-useable in future projects. 

Visit our 'project overviews' page for a jargon-free review of the projects listed above

Published Papers

Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag

Spatial and temporal modelling of incidence and prevalence of COVID-19

Estimating COVID-19 prevalence and transmission from multiple sources: de-biasing Pillar 2 data  

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic - ScienceDirect (


International Lecture Series 2022

Videos from the lectures can be found on YouTube.


Explaining the science

You can learn more about the individual projects listed above by visiting the project overview page.

Jargon-free and written for a quick and easy guide to the work that is taking place at the Turing-RSS Health Data Lab.

Contact info

To learn more or to get involved with our project work, contact the team at [email protected]

Follow us on Twitter @turingrss_hdlab