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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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Use of Machine Learning Techniques to Create a Credit Score Model for Airtime Loans

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  Airtime lending default rates are typically lower than those experienced by banks and microfinance institutions (MFIs) but are likely to grow as the service is offered more widely. In this paper, credit scoring techniques are reviewed, and that knowledge is built upon to create an appropriate machine learning model for airtime lending. Over three million loans belonging to more than 41 thousand customers with a repayment period of three months are analysed.  Logistic Regression, Decision Trees and Random Forest are evaluated for their ability to classify defaulters using several cross-validation approaches and the latter model performed best. When the default rate is below 2%, it is better to offer everyone a loan. For higher default rates, the model substantially enhances profitability. The model quadruples the tolerable level of default rate for breaking even from 8% to 32%.  Nonlinear classification models offer considerable potential for credit scoring, coping with higher levels
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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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A racing heart for St Valentine's day

Happy St Valentine's Day A racing heart might be a sign of love or fear ... Using a mathematical model of the electrocardiogram produced by a human heart, we speed up the heart to produce some interesting music. Synthetic ECG generator: ECGSYN --> McSharry PE, Clifford GD, Tarassenko L, Smith L. (2003). A dynamical model for generating synthetic electrocardiogram signals . IEEE Transactions on Biomedical Engineering 50(3): 289-294; March 2003.
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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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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 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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