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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 rem...
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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...
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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 ...
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