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How machine learning can save your life

Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

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Over 11000 people died during the 2014-2016 Ebola outbreak in West Africa.The virus hopped between Guinea, Leone, Nigeria and Liberia, before making its way to the UK and US. But what would have happened if analysis and machine learning stepped in to help solve the problem, asks ANESHAN RAMALOO of SAS.

But what if we could have predicted the outbreak months before it happened, buying us time to take proactive measures to contain it and curb its spread?

With access to overwhelming volumes of data, the computational power needed to store and analyse this data in real time, and sophisticated algorithms that can find patterns in the data and alert authorities to health problems before they become, well, problems, pandemics don’t have to be as devastating as they have been in the past.

In fact, with advanced data analytics, we can better manage any disease – long-term, short-term or pandemic – resulting in better patient treatment, more efficient use of resources and cost savings.

It’s been done before.

By analysing data from social media, blogs, online forums and keyword searches, we were able to predict the 2012-2013 US flu season three months before the Center for Disease Control (CDC) issued its first official warning.

Imagine the impact if the same analytical power was applied across the entire healthcare spectrum – not only on a national and global level, but right down to the individual level.

Data evolution

In the past, health workers relied on manually intensive, paper-based systems to record infections and deaths during disease outbreaks. Not only was it easy for errors to slip through but because the data was anecdotal and historical, authorities did not get a complete understanding of the reach and impact of the outbreak.

During the Ebola outbreak, the CDC adopted a mobile data collection system that enabled health workers to instantly submit information to a database via text messages. This low-cost method of information gathering not only resulted in fewer errors but also allowed analysts to draw up detailed maps of population movements, which made it easier to understand how the disease was likely to spread, and where to set up treatment centres.

While this was certainly an improvement on the paper-based systems of old, the drawback was that mobile data was historic and did not provide researchers with the ability to track developments and population movements in real time.

Data-driven action

But mobile phones are just one source of data. Today, health authorities can overlay thousands of data sources – including social media, health and physician reports, keyword searches, media reports, transactional data from retailers and pharmacies, airline ticket sales, geospatial data and more – to not only better manage diseases and outbreaks when they do happen, but to see them coming months in advance – and what could happen if we don’t act on the information.

By mining structured and unstructured data, we can track the movements of infected populations and who they come into contact with; we can measure the success of containment policies, education campaigns and treatments – and what to do if they’re not working; we can determine the effect of weather and other environmental factors on the spread of diseases.

Never before have we been able to act on information to save lives, not just during pandemics but through better understanding and treatment of diseases.

Personalised treatment

Until now, standard treatments for diseases such as cancer and HIV have been applied to all patients, regardless of their unique profiles and with little understanding as to why some people respond well to certain treatments and others don’t.

But by analysing and creating ‘medical maps’ of individuals that take into account their anatomy, physiology, DNA, RNA and chemical composition, doctors can prescribe personalised treatments that have a greater chance of success.

There are many other benefits of data analysis in healthcare:

·        Personalised treatment can result in fewer hospital admissions and can produce faster results and better experiences for patients;

·        By better understanding the impact of lifestyle and diet on health, medical aid providers can educate their members with the aim of improving their health, which could result in cost savings for both the provider and the member;

·        Governments can use data to develop proactive approaches to protecting and promoting public health, to prioritise services and to find ways to cut costs so that they can provide healthcare to more citizens.

·        By sharing data and results from clinical trials and combining that data with academic, patient and industry data, medical researchers can better understand the genetics of viruses, why some strains are more deadly than others, and why some people are more resistant to viruses. This could spark innovation and generate new insights that ultimately improve treatment and outcomes.

AI and machine learning

As the use of intelligent algorithms, machine learning and natural language processing becomes more entrenched in advanced data analytics, technology will increasingly supplement the skills of humans to produce faster and more accurate medical diagnoses.

We’re already seeing successful applications of artificial intelligence (AI) in predicting relapse in leukaemia patients and in distinguishing between different types of cancer.

Machine learning can extract valuable insights from unstructured data like clinical notes and academic journals to provide even larger datasets that will transform the medical industry into a proactive front against diseases.

There are plenty of doomsday theories about how machines will supersede our intelligence and rise against us. But there aren’t enough stories about the potential of data analytics, AI and machine learning to supplement human skills and knowledge to drastically changes lives for the better – and even save them. Right now, it’s looking more likely that machines will actually help us to live longer – and I don’t know many people who would object to that.

 

  • ANESHAN RAMALOO, ‎Data Scientist and Senior Business Solutions Manager at SAS.

Africa News

Africa phones go flat

Africa’s mobile phone market declined 2.1% quarter on quarter in Q3 2018 according to the latest figures from IDC.

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The global technology research and consulting firm newly released Quarterly Mobile Phone Tracker shows overall shipments for the quarter totalled 52.6 million units, with feature phone shipments falling 2.7% QoQ and smartphone shipments declining 1.3% over the same period.

Transsion brands (Tecno, Infinix, and Itel) led the feature phone space in Q3 2018, with a combined unit share of 58.2%. Nokia was next in line with 11.7% share. Transsion, Samsung, and Huawei dominated the smartphone space with respective unit shares of 34.9%, 21.7%, and 10.2%. However, in value terms, Samsung led the smartphone market with 37.2% share, followed by Transsion (21.0%) and Huawei (13.0%).

There were differing fortunes in the region’s three major markets, with Nigeria suffering a heavy 11.6% QoQ decline in mobile phone shipments, while South Africa and Kenya saw respective QoQ growth of 8.5% and 7.9% in Q3 2018.

“The decline in Nigeria stemmed from a slowdown in government spending, ongoing warfare in the country’s northern states, and market uncertainty in the lead up to elections,” says George Mbuthia, a research analyst at IDC. “In South Africa, the market’s growth was spurred by the penetration of low-end devices from brands such as Mobicel, Mint, and Nokia, while the launch of entry-level smartphones helped drive growth in Kenya despite increases in taxes and fuel prices placing a significant burden on disposable income in the country.”

While feature phones remain steadfastly popular across Africa, particularly in more rural areas, consumers are increasingly being attracted by smartphone offerings from Chinese brands such as Xiaomi, Oppo, and Huawei, which are actively targeting feature-oriented customers at more economical price points.

“There is a new wave of Chinese brands aggressively pursuing growth opportunities in the region, while the more-established Huawei is also accelerating its marketing efforts and expanding its distribution budget,” says Ramazan Yavuz, a research manager at IDC. “These brands have quickly progressed along the learning curve and evolved their offerings to perfectly reflect the realities of the region by addressing the diverse pricing and feature needs of the consumer base.”

Looking ahead, IDC expects Africa’s overall mobile phone market to reach 58 million units in Q4 2018, spurred by the festive season and online consumer events such as Black Friday. The introduction of more affordable smartphones in the African market will help drive progress in this space over the coming quarters, while the share of feature phones will decline steadily as the transition to smartphones gathers momentum.

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Africa News

Mobile money to cross borders

Orange and MTN launch pan-African mobile money interoperability to scale up mobile financial services across Africa.

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Two of Africa’s largest mobile operators and mobile money providers, Orange Group and MTN Group, today announced a joint venture, Mowali (mobile wallet interoperability), to enable interoperable payments across the continent. Mowali makes it possible to send money between mobile money accounts issued by any mobile money provider, in real time and at low cost.

Mowali will immediately benefit from the reach of MTN Mobile Money and Orange Money, bringing together over 100 million mobile money accounts and mobile money operations in 22 of sub-Saharan Africa’s 46 markets. Mowali is ready to enable interoperability between digital financial service providers beyond MTN and Orange operations and markets, to support the existing 338 million mobile money accounts in Africa.

Mowali is a digital payment infrastructure that connects financial service providers and customers in one inclusive network. It functions as an industry utility, open to any mobile money provider in Africa, including banks, money transfer operators and other financial service providers.

The objective of Mowali is to increase the usage of mobile money by consumers and merchants.  Mowali enables money to circulate freely between mobile money accounts from any operators in all countries.  From the customer’s point of view, this means “I can pay or receive money anywhere from my mobile account regardless of my operator”. The system will unlock further innovation in the digital financial space within the continent. 

For Stéphane Richard, Chairman & CEO of Orange, “by providing full interoperability between platforms, Mowali will provide an important step forward that will allow mobile money to become a universal means of payment in Africa. Increasing financial inclusion through the use of digital technology is an essential element in furthering the economic development of Africa, particularly for more isolated communities. This solution embodies Orange’s ambition to be a leading player in the digital transformation of the continent. By joining forces with another of Africa’s market leaders, MTN, we aim to accelerate the pace of this transformation in a way that will change the lives of our customers by providing them with simpler, safer and more advantageous services. “

“One of MTN’s goals is to accelerate the penetration of mobile financial services in Africa, Mowali is one such vehicle that will help us achieve that objective. Furthermore, co-operation and partnerships that help us accelerate the pace of development and overcome some of the scale, scope and complexity of challenges that society faces are key. This partnership with Orange is therefore an important step in helping us play a meaningful role in supporting the United Nations’ Sustainable Development Goals related to eliminating extreme poverty and enhancing socio-economic development in the markets we operate in and beyond. Thus giving our customers access to a bright, digital future.” said Rob Shuter, Group President and CEO of MTN.

The GSMA supports the Mowali initiative as interoperability at this scale is a key accelerator for both financial inclusion and Mobile Money usability across Africa.  “Today, there are over 690 million mobile money accounts around the world. Mobile money services have become an essential, life-changing tool across Africa, providing access to safe and secure financial services but also to energy, health, education and employment opportunities. The creation of Mowali will help to further transform mobile financial services throughout the African region. It demonstrates the mobile industry’s continued leadership and commitment to driving financial inclusion and economic empowerment through industry collaboration. The GSMA is proud to support its development,” said Mats Granryd, Director General, GSMA.

“Interoperability of digital payments has been the toughest hurdle for the financial services industry to overcome, in support of financial inclusion. With Mowali, Orange and MTN deliver a solution that will enable them, and other companies, to scale digital financial services across Africa, faster, to everyone—including the poor,” said Kosta Peric, deputy director of Financial Services for the Poor, at the Bill & Melinda Gates Foundation “This is a signal that a new wave of innovation, which can help alleviate poverty and drive economic opportunity, is coming. We’re pleased to see an implementation of Mojaloop—an open source payment platform available to operators across the sector—help achieve that.”

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