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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Forecasting demand using Big Data

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As we walk, cycle or drive around Oxford, make telephone calls, send texts or emails and do our shopping, many of us are unaware of exactly how much data is being generated by our activities. "Big data" is a catch-phrase for describing the overwhelming volume, velocity and variety of this stream of information. Big data has the potential to provide many opportunities for the public and private sectors, offering a means of fusing different sources of information and supporting decision-making in real-time. Perhaps the most interesting aspect of big data is how it deepens our understanding of human behaviour seen through the collective actions of many individuals. We tend to consume services following the temporal cycles in our everyday lives. There are three evident cyclical patterns based around the hour of day, the day of the week and the season of the year. All of these patterns can be seen in electricity consumption, call centre activity, internet usage, financial tran
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Forecasting the number of potential coronavirus cases

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Chinese health authorities reported a group of cases of viral pneumonia to the World Health Organization (WHO) in late December 2019 and this new virus is now referred to as 2019-nCoV. The number of cases is almost eight thousand and is growing at an alarming rate of 40% per day. A total of 170 people have died as a result of the virus. The coronavirus appears to be contagious before symptoms appear with an incubation period of 10 to 14 days, making it incredibly difficult to identify cases. The number of cases reported by the National Health Commission in China and the WHO are crucial for facilitating decision-making about the virus.  A wikipedia website is dedicated to collecting the  timeline  statistics for the virus [1]. By plotting the number of confirmed cases it is apparent by eye that the numbers are growing exponentially which is demonstrated by the straight line on the above log-scale chart. If the virus maintains this trajectory, the number of cases could surpass 100,0
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