The alternative option is to increase transparency. Instead of analysing people’s lives without their knowledge, let them weigh up the benefits and risks. Involve them in the debates; think in terms of permission rather than forgiveness. If social benefits are the aim, make the research a social effort. When the nhs announced their ‘Care.data’ scheme in 2013, the hope was that better data sharing could lead to better health research. Three years later, the scheme was cancelled after the public – and doctors – lost confidence in how the data were being used. In theory, Care.data could have been enormously beneficial, but patients didn’t seem to know about the scheme, or didn’t trust it.[29]
Perhaps nobody would agree to data-intensive research if they knew what was really involved? In my experience, that’s not necessarily true. Over the past decade, my collaborators and I have run several ‘citizen science’ projects combining contagion research with wider discussions about outbreaks, data, and ethics. We’ve studied what networks of interactions look like, how social behaviour changes over time, and what this means for infection patterns.[30] Our most ambitious project was a massive data collection effort we ran in collaboration with the BBC during 2017/18.[31] We asked the public to download a smartphone app that tracked their movements to the nearest 1km over a day, and also asked them to tally up their social interactions. Once the study was completed, this dataset would help form a freely available resource for researchers. To our surprise, tens of thousands of people volunteered, despite the project having no immediate benefit to them. Although just one study, it shows that large-scale data analysis can still be carried out in a transparent and socially beneficial way.
In March 2018, the BBC aired a program called Contagion!, showcasing the initial dataset we’d gathered. It wasn’t the only story about large-scale data collection in the media that week; a few days earlier, the Cambridge Analytica scandal had broken. Whereas we had asked people to volunteer their data to help researchers understand disease outbreaks, Cambridge Analytica had allegedly been harvesting vast quantities of Facebook data – without users’ knowledge – to help politicians try and influence voters.[32] Here were two studies of behaviour, two massive datasets, and two very different outcomes. Several commentators picked up on the contrast, including journalist Hugo Rifkind in his TV review for The Times. ‘In a week when we’ve agreed that data and internet surveillance – boo, hiss – are ruining the world, Contagion was a welcome reminder that it can sort of save it a bit too.’[33]
In the time it’s taken you to read this book, around three hundred people will have died of malaria. There will have been over five hundred deaths from hiv/aids, and about eighty from measles, most of them children. Melioidosis, a bacterial infection that you may well have never heard of, will have killed more than sixty people.[34]
Infectious diseases still cause vast damage worldwide. As well as known threats, we face the ever-present risk of a new pandemic, and the rising emergence of drug-resistant infections. However, as our knowledge of contagion has improved, infectious diseases have on the whole declined. The global death rate for such diseases has halved in the past two decades.[35]
As infectious diseases wane, attention is gradually shifting to other threats, many of which can also be contagious. In 1950, tuberculosis was the leading cause of death for a British man in his thirties. Since the 1980s, it has been suicide.[36] In recent years, young adults in Chicago have been most likely to die from homicide.[37] Then there are the wider social burdens of contagion. When I analysed neknomination back in 2014, online transmission seemed like a tangential issue, almost a curiosity. Three years later, it was dominating front pages, with concerns about the spread of false information – and the role of social media – leading to multiple government investigations.[38]
As our awareness of contagion increases, many of the ideas honed in the study of infectious diseases are now translating to other types of outbreaks. After the 2008 financial crisis, central banks latched onto the idea that the structure of a network could amplify contagion, a theory pioneered by STI researchers in the 1980s and 1990s. Recent efforts to treat violence as an infection – rather than simply a result of ‘bad people’ – echo the rejection of diseases caused by ‘bad air’ in the 1880s and 1890s. Concepts like the reproduction number are helping researchers quantify the spread of innovations and online content, while methods used to study pathogen sequences are revealing the transmission and evolution of culture. Along the way, we’re finding new ways to speed up beneficial ideas and slow down harmful ones. Just as Ronald Ross hoped in 1916, a modern ‘theory of happenings’ is now helping us analyse everything from diseases and social behaviour to politics and economics.
In many cases, this has meant overturning popular notions of how outbreaks work. Like the idea that we need to remove every last mosquito to control malaria, or vaccinate every person to prevent epidemics. Or the assumption that banking systems are naturally stable and online content is highly contagious. It has also meant hunting for new explanations: why cases of Guillain-Barré Syndrome were appearing on Pacific Islands, why computer viruses persist for so long, why most ideas struggle to spread
