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

Ongoing

Introduction

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

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

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

Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers

A large-scale and PCR-referenced vocal audio dataset for COVID-19

Pre-prints

 

Blogs

Why COVID-19 test data is skewed, and what we’re doing to fix it 

South Asians in poorer areas more at risk of catching COVID-19

Can the sound of someone’s cough be used to detect COVID-19?

 

International Lecture Series 2022

The International Lecture Series was based on three themes around responses to health emergencies:

  • Statistical modelling and machine learning approaches
     
  • Policy responses 
     
  • International best practice in responding to national health emergencies

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