Research




NERC - Probability, Uncertainty & Risk in the Environment

 PURE: knowledge exchange network

 Collaborative Oxford Network for Bipolar Research to Improve Outcomes

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Remote health diagnosis and monitoring in the time of COVID-19

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Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms.  The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotel
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Using big data to turn global catastrophic risks into opportunities

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Big data is currently transforming both the public and private sectors by increasing efficiency, transparency and productivity whilst also promoting sustainability. As the ability to utilise intelligent data analytics distinguishes today’s winners, data is fast becoming the oil of the 21st century. Organisations and countries that manage to harness this new commodity will ensure sustainable economic growth in the same way that those with access to cheap fossil fuel resources have been in an advantageous position in the past.   The proliferation of mobile technology, wireless sensors, social media and the Internet of Things, provides a means of monitoring socio-economic activity, consumption of resources, transactions, human mobility and environmental change. Recent advances in data science are now capable of coping with the technical challenges of collecting, managing and developing actionable insights from big data. Much of the exciting research has focused on addressing the t
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Forecasting tea productivity using local weather conditions

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Tea plantation in the western province of Rwanda. The global tea market is estimated to be worth about 38.2 billion U.S. dollars in 2016 and is projected to continue growing at 2.8% annually until 2020 [1]. After coffee, tea is Rwanda's second most valuable export where agriculture accounts for one third of economic output [2]. Tea productivity depends on a various factors such as weather, fertiliser and management practices. Hot dry conditions are generally detrimental for tea productivity and therefore daily rainfall and temperatures are important variables. A recent drought in Rwanda, claimed to be the worst in 60 years, has generated losses of up to 40% for some tea producers. Climate variability is already a concern for the agricultural sector.  Uncertainty about the impact of future climate change and the frequency and severity of weather extremes such as droughts poses substantial challenges for policymakers. Information about the current climate relies on 30-year
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