Two Informatics projects gain DDI funding to aid local Covid-19 recovery
Two projects led by Rik Sarkar and DK Arvind have received funding from the Data-Driven Innovation (DDI) Programme to support local responses to the Covid-19 pandemic.
In response to the Covid-19 pandemic, the Data-Driven Innovation Programme allocated almost a quarter of a million pounds in small grants to enable staff and students at the University to apply data-driven-innovation ideas in support of communities, services and businesses in Edinbugh and the South-East Scotland region. Nineteen projects over three rounds were approved for funding, including those proposed by Rik Sarkar and DK Arvind from the School. Projects led by Michael Rovatsos, of the Bayes Centre, and David Murray-Rust, from Design Informatics, were also awarded grants.
Rik Sarkar and SIM-SPREAD
Rik Sarkar was awarded £17,000 to work on his project SIM-SPREAD: Data Driven Simulation and Modelling for Infection Spread Reduction and Cultural and Economic Reopening in Edinburgh. This project will model the spread of viral infection to provide recommendations on strategies for lockdown, reopening and social distancing. Closure of business and cultural activities due to Covid-19 imposes a significant social and economic loss. The project will use probabilistic simulation and statistical modelling based on real data to gain actionable insights. Special attention will be paid to cultural activities and festivals, which are integral to life and economy in Edinburgh City. The cross disciplinary team for the project includes advisors from governance, transportation, epidemiology and event management; the background of the researchers span computer science, economics and business.
DK Arvind and the Respeck device
DK Arvind (Centre for Speckled Computing) was awarded £20,000 to work on his project Monitoring COVID-19 Patients using the Respeck device at the Lothian Regional Infectious Diseases Unit (RIDU). The project brings together a multidisciplinary team of clinicians at the Western General Hospital. The pulmonary manifestation of infection of the severe acute respiratory syndrome SARS-CoV-2 is the inflammation of lung tissues, and the exhibition of patterns of dyspnea - shortness of breath, and elevated respiratory rates above 30 breaths/minute. Current practice in hospital wards is for nurses to estimate manually and record the respiratory rate at hourly intervals with the exact period based on the prognosis of the attending physician. The hypothesis of this study is that continuous respiratory monitoring (minute-level frequency) may reveal underlying trends and patterns that are missed when only using the current manual snapshot measurements.
The Respeck device, developed in the Centre for Speckled Computing, School of Informatics at the University of Edinburgh, is worn as a plaster on the chest and transmits wirelessly continuous measures of the respiratory rate/flow to a mobile phone for onward transmission to the GoogleCloud for storage. The Respeck time-series data will be analysed using a selection of machine learning based methods for clearly identifiable patterns, with good sensitivity and specificity that could be used to predict deterioration in Covid-19 patients.
The patients in the isolation rooms at the Lothian Regional Infectious Diseases Unit (RIDU) in the Western General Hospital will each be attached with a Respeck on admission. The latest minute average of the respiratory rates for all the patients and their trends over the previous four to six hours can be viewed in a dashboard on a mobile device such as a tablet. At a glance the nurses can view and interrogate the status and trends in the respiratory rate for all the patients in the hospital ward. Qualitative data will be gathered from the nurses and doctors using a mixture of questionnaires and interviews on their perception of using continuous respiratory monitoring, and its practicability and acceptability on the wards as feedback for improvements. Future plans include remote monitoring using the Respeck sensor of Covid-19 recovered patients in their homes, as well as the elderly with morbidities, such as diabetes, high blood pressure, asthma and COPD, to detect early signs of deterioration in their respiratory function.
£243,000 has been awarded to 19 projects across the University that will use data-based approaches to benefit businesses, communities and services in Edinburgh and South-East Scotland. The funding has been provided by the Data-Driven Innovation programme’s Response and Recovery scheme. Other projects include initiatives encouraging greater local food production, tackling mental health issues exacerbated by lockdown, and assessing the impacts on care provided to pregnant women. The projects will involve collaboration with partners including the Scottish Government, NHS Lothian and local social enterprises.
The Data-Driven Innovation programme is part of the Edinburgh and South East Scotland City Region Deal, which aims to accelerate productivity and inclusive growth by funding infrastructure, skills and innovation.
Considering the short space of time applicants were given, the 36 applications we received were outstanding. Because of this, and our aim to help the Edinburgh and South East region recover from Covid-19, we doubled the funding pot. Data innovation has the capacity to improve the livelihoods and medical treatment of those most affected by the virus. The 19 projects awarded will link academics with local and global organisations to deliver solutions using data innovation that assist our region in its recovery.