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Data science can reduce likelihood of virus outbreaks

Data science is of vital importance in preventing future outbreaks of viruses, Professor of Data Science Aske Plaat argues. Inaugural lecture entitled ‘Data Science and Ebola’ on 13 April.

Oceans of data transformed into knowledge

In the modern world everyone is continuously producing data. The number of internet users is still expanding rapidly, fuelling the growth of government, medical and corporate databases that are a treasure-trove of information. This means that the science that enables the efficient transformation of that data into knowledge, data science, is becoming increasingly important. In his inaugural lecture, Plaat will argue that this also applies to public health throughout the world. He will discuss the possibilities and limitations of data science through a case study of the Ebola outbreak in West Africa in 2014. As the World Health Organization (WHO) corroborates, the lack of reliable data greatly exacerbated the seriousness of that outbreak.

Ebola difficult to diagnose

Systems and techniques for gathering and collating data have improved greatly over the last ten years. This has enabled data-driven research to really take off in a number of fields, including astronomy, biology and sociology. Plaat argues that these innovative data processing techniques can also play an important role in dealing with the spread of Ebola. During last year’s outbreak, scientists found it difficult to diagnose the disease due to the absence of simple tests and the slow rate at which data was collected. This made it difficult to locate the disease, enabling it to spread without being detected. Tests that can even be administered by untrained personnel could have prevented this, such as the use of mobile phones and apps complete with lists of symptoms. These could have been used to increase the rate of data collection, making it easier for scientists to combat and contain the disease.

Data must be made public

It’s crucial that governments and health organisations make their information public in these kinds of situations, Plaat argues. Unfortunately they are usually overly protective of their data. Governments and health organisations, including the WHO, only resorted to sharing their data once the huge size of this outbreak became apparent. Only when that point was reached were scientists able to improve their online models, predict the spread of the disease and facilitate the manufacturing of vaccines and medicines. These collaborative open data initiatives, Plaat argues, are of crucial importance. This way data science can be used to reduce the likelihood that future outbreaks will occur and, when they do, save as many lives as possible.

Aske Plaat

Aske Plaat (1966) is Professor of Data Science. He has previously studied complex networks and machine learning, including at the Supercomputing Technologies Group at MIT. He has won several awards for his work in finding intelligent searching algorithms and is also the director of the Master’s programme on ICT in Business at the LIACS. 
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