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Durham University

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Helping health services plan for Covid-19

(25 June 2020)

Our researchers are working with local hospitals and local authorities as they move into the next phase of Covid-19 planning.  Here, Dr Camila Caiado, from the Department of Mathematical Sciences, and Prof Brian Castellani, from Sociology, tell us more.

Q: Tell us about your research

The research, which is primarily housed in the Department of Mathematical Sciences, is based on a process which uses data and statistics to forecast outcomes, something known as predictive modelling.

By using our expertise in data modelling, we are able to help local NHS hospital trusts monitor and evaluate hospital capacity and patient outcomes. While this work has been ongoing for some time through Dr Caiado’s team, the research has become particularly helpful in the current Covid-19 pandemic, a time when NHS and council services have faced increased demand.

Our Sociology Department is also involved in this work, looking at how factors such as where people live affect the number of Covid-19 cases in communities. They are also helping us look at how various exit strategies for easing social distancing will impact the spread of the virus in different communities.

Q: How is your research helping local NHS services?

We’re using our research to help local hospital trusts – County Durham and Darlington NHS Foundation Trust, Sunderland and South Tyneside NHS Foundation Trust and South Tees Hospitals NHS Foundation Trust, as well as local authorities – to predict and understand outcomes for Covid-19 patients and the wider community.

Our models can be used for a range of purposes. For example, to support planning for critical care capacity by hospitals and to investigate effectiveness of treatment for different groups of patients.

Q: Why is this research so significant during the Covid-19 pandemic?

Throughout the Covid-19 pandemic, the number of cases and the spread of the virus has varied across regions. We saw that London experienced a high number of cases early on, whereas areas such as the North East have peaked much later.

There are various different factors that account for how Covid-19 spreads and affects communities, such as health vulnerabilities, geographical location, population size, and access to health care.

By taking into account these variations, we can make more realistic short and medium-term predictions at a regional level, helping us to inform how local strategies are developed in response to Covid-19.

Q: What are the next steps with the research?

Our plan is for these models to be used to support decision making around the re-opening of currently suspended non-critical services, such as routine surgery, identifying areas of high risk within our local authority areas, and helping curb the future spread of the virus.

We’ll also continue to work with the Academic Health Science Network for the North East and North Cumbria, an organisation which works with regional health organisations, to support the longer-term planning of health and social care.