Data science and linked data aiding understanding of the unfolding Covid-19 epidemic in Wales
Written by 14 May 2020
ADR Wales is part of a cross-institutional team leveraging existing and new datasets to provide evidence to inform policy and reduce the impact of the Covid-19 epidemic in Wales. Ashley Akbari, Senior Research Manager & Data Scientist at Swansea University, discusses the importance of the work.
The Covid-19 pandemic is a public health crisis that also threatens the capacity of the health services to respond to need. Current disease models show that Covid-19 has spread widely across the UK and is rapidly evolving. The lag between symptoms, test results, clinical disease and outcomes means that it will take weeks and sometimes months to understand all consequences of different courses of action.
Factors such as time for accrual of large numbers of cases, data siloes, multiple data flows across organisations and sectors, focus on acute response priorities and a shortage of data science skill sets could hinder the depth of understanding needed to optimise policy decisions.
Collaboration in a time of crisis
In keeping with the One Wales philosophy, we have quickly created a cross-institutional team of data scientists, biological scientists, clinicians and policymakers who have come together to leverage existing and new datasets and apply their expertise to provide timely evidence to inform policy and practice in order to reduce the impact of the epidemic on the Welsh population.
Getting detailed insight to guide targeting and optimisation of control efforts to maximise impact requires rapid analysis of a multitude of linked datasets. Details on risk factors, infection status and outcomes at an individual level are essential. Outcomes happen to individuals; risk adjustment and evaluation of interventions requires individual and not aggregate data.
Fortunately, Welsh Government’s investment in the Secure Anonymised Information Linkage (SAIL) Databank since 2008 means that Wales is an internationally recognised leader in the field of privacy-protecting data linkage. SAIL can monitor the impact of a very wide range exposures and outcomes on the entire population using robustly de-identified data. It is possible to track the development of health conditions in individuals and nested in households and school populations, monitor the development and spread of diseases, and evaluate the impact of exposures and the effects of treatments on outcomes.
As such to date, through a Wales-wide collaboration a phenomenal amount of activity has already been completed to undertake the governance discussion, data acquisition and preparation of data to begin to create a Wales national e-cohort which will be used to study and investigate potentials trends and outcomes and model strategies for Covid-19 response for Wales.
We wish to acknowledge the collaborative partnership that is enabling this work, led by the Swansea University Health Data Research UK (HDR UK) team under the direction of the Welsh Government Covid-19 Technical Advisory Cell (TAC) and Technical Advisory Group (TAG) and the Scientific Advisory Group for Emergencies (SAGE). The partnership includes: the SAIL Databank, ADR Wales, NHS Wales Informatics Service (NWIS), Public Health Wales and NHS Wales Shared Services, members of the public and academic groups from Swansea and Cardiff universities. All research conducted has been completed under the permission and approval of the SAIL independent Information Governance Review Panel (IGRP) project number 0911.
This work uses data provided by patients and collected by the NHS as part of their care and support. We would also like to acknowledge all data providers who make anonymised data available for research.
The weekly reports to SAGE can be found on HDR UK website – A national health data research capability to support COVID-19 research questions.
Health Data Research UK (Wales & Northern Ireland) is one of the nine Centres of Excellence based in Population Data Science at Swansea University Medical School.
This article was originally published on the Swansea University Medical School Population Data Science website.