Gathering, processing and interpreting data sets is what runs the modern global economy. Everything from your weekly online supermarket grocery shop, to how the shelves get stacked with goods delivered by cargo ships from all across the world. Teams of statisticians make their daily bread from finding more and more sophisticated ways of predicting human behaviour.
Complex mathematical modelling is also being used in humanitarian emergency situations to prevent further loss of life. I’m not just referring to how aid is delivered through more efficient supply chains, but to how the mapping of crises and outbreaks of diseases is used to instigate the correct response and predict how the spread is evolving.
Dr Sebastian Funk at the London School of Hygiene and Tropical medicine is a leader in the field and was at the centre of important work to map the Ebola outbreak in West Africa. By processing data collected from the affected area Dr Funk and peers across the world were able to look almost 6 weeks into the future, with 95% confidence of the first week.
Finding a ‘magic bullet’ or key to suppressing an outbreak is time sensitive – one must collect enough quality data to make sure that the models can be accurate, but when people’s lives are at stake conclusions need be drawn quickly.
It was found that ‘most people infected with the deadly virus became ill through contact with a small number of so-called ‘superspreaders’ and ‘if superspreading had been under control, about two-thirds of Ebola cases could have been avoided’.
Here he is speaking to RFI’s English service
Dr Funk’s work was reliant on effective feedback on the ground. He knew that whilst cases might be dropping off, there are unreported areas and if the superspreaders had been identified earlier, lives could have been saved.
The unprecedented rise of smartphone and social media has changed the data landscape. Agencies can have access to crowd-sourced information, which taken together can be highly accurate.
This was already happening during the Ebola outbreak – ‘When epidemiological data are scarce, social media and Internet reports can be reliable tools for forecasting infectious disease outbreaks’, from findings published in the Journal of Infectious Diseases.
Patrick Meier advocates the use of geographically mapping social media posts – in Digital Humanitarians: how BIG DATA is changing the face of humanitarian response he describes crisis mapping of the 2010 Haitian earthquake and Libyan political turmoil in 2011 known as the Arab Spring. This approach allows aid workers to have a more complete live overview of the situation that’s constantly updated.
The crowd-sourced reports are collated by a volunteer team that work 24hrs a day, corroborating information from social media sources. It led to a significant change in the response, something which was praised by UNOCHA, if not without some reservations.
In this case, the role of the human as an arbiter of trustworthiness remains a significant undertaking. Even with pleas like: ‘we just need to make sure you’re not Gaddafi!…we are not Facebook!’ (Meier, p.125) for people to declare their background information, the digital gathering of sources, even on a big scale, still has to have an element of journalistic rigour.
The future will be to use artificial intelligence to perform checks that people simply don’t have the capacity to do, given the volume of information coming in. Systems are being developed that analyse qualitative rather than quantitative qualities of posts, allowing computers to detect false rumours and unrelated background noise.
With accurate models, the prediction capabilities in future outbreaks of disease or disaster will certainly be enhanced, leading to an untold impact on lives saved.
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References. Links in text plus:
Mier, Patrick. Digital Humanitarians: How BIG DATA Is Changing the Face of Humanitarian Response CRC Press, London. 2015