As a new disease spreads rapidly around the world, collective intelligence is being harnessed to predict, monitor, and find solutions to the COVID-19 pandemic. Here’s how.
Tackling the emergence of a new global pandemic is a complex task. Here we show how collective intelligence approaches are being used around the world by communities and governments to respond.
At its simplest, collective intelligence is the enhanced capacity created when distributed groups of people work together, often with the help of technology, to mobilise more information, ideas and insights to solve a problem.
Advances in digital technologies have transformed what can be achieved through collective intelligence in recent years - connecting more of us together, augmenting human intelligence with machine intelligence, and helping us to generate new insights from novel sources of data. It is particularly suited to helping address fast-evolving, complex global problems like disease outbreaks.
Collective intelligence is already being harnessed to make positive contributions to stemming the COVID-19 outbreak.
On the 31st December 2019, health monitoring platform Blue Dot alerted its clients to the outbreak of a flu-like virus in Wuhan, China - nine days before the World Health Organisation released a statement about it. It then correctly predicted that the virus would jump from Wuhan to Bangkok, Seoul, Taipei, and Tokyo.
Blue Dot uses a typical collective intelligence technique of combining existing data sets to generate new insights. Natural language processing and machine learning techniques sift through reports of disease outbreaks in animals, news reports in 65 languages, and airline passenger information. It supplements the machine-generated model with human intelligence, drawing on a diversity of expertise from epidemiologists to veterinarians and ecologists to ensure that conclusions are valid.
In 2018 the BBC carried out a citizen science project, which involved members of the public in generating new scientific data about how infections spread. People downloaded a mobile phone app that monitored their GPS position every hour, and asked them to self-report who they had encountered or had contact with that day. This collective intelligence initiative created a huge wealth of data that helped researchers understand who the super-spreaders are, as well the impact of control measures on slowing an outbreak. Although the full data set is still being analysed, researchers have released the social contract matrices to help with modelling the UK's response to COVID-19.
Crowd prediction polling platforms Metaculus and the Good Judgement project are both running coronavirus related questions including the expected number of infections and impact on the markets. This approach aggregates many individual forecasts from different people to produce a ‘wisdom of the crowd’ score. The theory behind crowd forecasting is that errors in individual predictions caused by partial information or bias tend to be cancelled out when those from many people are combined. To test this, Nesta ran our own crowd prediction polling challenge last year. We found that the crowd was impressively accurate on Brexit-related questions, but fared much less well on forecasts relating to the numbers of measles infections in the US, and the spread of Ebola. We will be watching these with coronavirus crowd predictions with interest.
Created by a coding academy based on official government data, COVID-19 SG allows Singapore residents to see every known infection case, the street where the person lives and works, which hospital they got admitted to, the average recovery time and the network connections between infections. Despite concerns about potential privacy infringements the Singapore government has taken the approach that openness about infections is the best way to help people make decisions and manage anxiety about what is happening.
For dashboard enthusiasts, MIT’s Tech Review has a good round-up of the many coronavirus-related dashboards currently tracking the pandemic.
In early February, Wired reported how researchers at Harvard’s medical school were using citizen-generated data to monitor the progress of the disease. To do this they mined social media posts and used natural language processing to look for mentions of respiratory problems, and fever in locations where doctors had reported potential cases. This builds on evidence published in a January article of the journal Epidemiology that found that hot spots of tweets could be good indicators of how a disease spreads. It remains to be seen how effective these initiatives are, or whether they will succumb to the problems that beset Google Flu Trends.
The reality of people’s lived experience of the virus is largely absent from media reporting so far, but the importance of social sciences in pandemic preparedness and response is becoming increasingly recognised. We should therefore all tip our hats to the citizens of Wuhan who have been archiving and translating social media data from inside China creating chronicles of testimonies of those affected, before they get censored by the government.
Inside Taiwan communities have been working together to crowdsource maps showing where face masks are still available to buy and the numbers available in different pharmacies. It’s this kind of mapping of assets, resources and even skills - and matching to needs - that can help communities develop resilience in times of emergencies.
To speed up the development of drugs to combat coronavirus, researchers at the University of Washington are calling on scientists and the public to play an online game. The challenge is to build a protein that could block the virus from infiltrating human cells. The game is on Foldit, a 12-year-old website which has crowdsourced contributions to important protein research from more than 200,000 registered players worldwide.
Responding to concerns about lack of access to testing for COVID-19, Nesta Collective Intelligence grantee Just One Giant Lab is behind an effort to develop a cheap, quick coronavirus test that can be used anywhere in the world. The initiative is crowdsourcing ideas from Do-It-Yourself biology communities, with the ambition to open source and share designs so that certified labs can easily produce test kits for their communities.
In a global crisis sharing of collective intelligence about the virus will be a significant factor in our ability to respond and find new treatments. NextStrain pulls in all the data from labs around the world that are sequencing SARS-CoV-2’s genome, and centralises it in one place for people to see in a genomic tree. This open repository, which is built on GitHub, is helping scientists studying cornavirus’s genomic evolution and enabling tracking of how the virus is passed between individuals.
Researchers have also been sharing new findings about the virus’ genomic profile through open source publications and preprint sites such as BioRxiv and Chinaxiv. Paywalls are being temporarily lifted on content related to coronavirus in scientific publications such as British Medical Journal and the public is demanding that major news outlets follow suit. Activists on Reddit have gone one step further and bypassed paywalls to create an open archive of 5,312 research articles mentioning coronaviruses, citing a “moral imperative” for the research to be openly accessible. Newspeak House is currently crowdsourcing a handbook of tools, tech and data for technologists building things to respond to the coronavirus outbreak.
The World Health Organisation (WHO) is compiling all published research into a global database, and making learning resources about managing COVID-19 for health professionals and decision makers available on the WHO online learning platform. But they have also been criticised for not replying to comments left on their channels, leaving a vacuum instead of a response to rumours and falsehoods.
Wikipedia is of course one of the most familiar collective intelligence projects. Since the coronavirus pandemic page was created in early January it has had over 12,089 edits averaging 232 a day, with over 800 individual people contributing to keep it updated.
As the above examples show, collective intelligence is already being harnessed to make positive contributions to stemming the COVID-19 outbreak. Sadly, there are also examples of how these same tools can also be used to drive collective stupidity and increase the risk of transmission.
From conspiracy theories to bogus medicines and whipped-up xenophobia, misinformation about the virus has proliferated on social media. It has led the WHO to assert that it is facing an infodemic. Services like FullFact are valiantly providing fact checking on claims around the virus - crowdsourcing examples of misleading claims and setting them straight - but these efforts are fighting a huge tide of misinformation.
South Korea is using an app for quarantined patients to monitor symptoms, with GPS tracking to make sure people don’t go out of quarantined locations. In China the government has been using mobile apps including Alipay to track and prevent infected people from travelling. The aggressive measures seem to have slowed the progress of the virus, but a New York Times investigation revealed the app is also sharing data with the police. It has prompted concerns that new forms of automated social control will persist after the epidemic subsides.
At Nesta’s Centre for Collective Intelligence Design we’ll keep tracking how collective intelligence is being used during the current crises, and updating this blog and our public Trello board of collective intelligence projects as often as we can. Please share any examples you come across in the comments.
For more on collective intelligence see Nesta’s Collective Intelligence Design Playbook.
A version of this blog was originally published on The Conversation.