BBC Contagion experiment offers insights into Covid-19 control
Data from a BBC citizen science experiment has helped predict how different strategies could control the spread of Covid-19 – according to new preliminary research from Oxford University, University of East Anglia, and the London School of Hygiene & Tropical Medicine.
The team of researchers re-purposed the BBC data to simulate outbreaks of Covid-19, and see which public health interventions might be effective.
The new study, published today, shows that tracing and quarantining the contacts of infectious people can be more effective when combined with large-scale testing and social distancing. Importantly, these approaches reduce the number of people needing to be quarantined at any given point in time.
Dr Josh Firth, from the Department of Zoology at Oxford University and joint-lead author of the study, said: ‘Using a real social network provides a great way of re-imagining how Covid-19 spreads within populations and how control strategies can be implemented in the real-world.
‘Human social behaviour is obviously very complex, so it is important that we are able to see how people socialise and interact in real life, rather than relying entirely on simulations of social interactions within communities.’
Contagion! The BBC Four Pandemic was a massive citizen science experiment that collected social contact and movement data using a custom-made phone app – in order to see how a future pandemic could spread across the UK.
The project was a UK-wide citizen science experiment set up by researchers at the University of Cambridge and London School of Hygiene & Tropical Medicine in 2017/18 to study how a future pandemic might spread. The documentary makers collected three days’ of mobile phone app-based tracking data of hundreds of volunteers within the Haslemere area of Sussex.
The new study came about through the Royal Society’s Rapid Assistance in Modelling the Pandemic (RAMP) Initiative, which aims to bring in wider modelling expertise to support the UK Covid-19 response.
A major challenge with predicting how well contact tracing could work is that real-world social tracking data is very limited. This is why the research team repurposed social networking data from Contagion! The BBC Four Pandemic.
The new research applied what is known about Covid-19 transmission to the ‘Haslemere social network’ and investigated a range of Covid-19 control strategies.
The researchers combined the real-world social network with mathematical models to predict how well testing, social distancing and quarantining the contacts of infected people would help control outbreaks of coronavirus.
Dr Lewis Spurgin, from UEA’s School of Biological Sciences and joint-lead author of the study, said: ‘We wanted to find out how contact tracing can be best used to stop the spread of Covid-19 – not least because more disruptive interventions, such as lockdowns, cannot be sustained for a long period of time.
‘Our epidemic model showed that uncontrolled outbreaks typically resulted a significant proportion of the population becoming infected.
‘We found contact tracing reduces outbreak size, but can result in lots and lots of people getting quarantined as the outbreak grows.’
The study also showed that social distancing, and large-scale testing and releasing of non-infectious people, would reduce the number of those in quarantine without large increases in outbreak size.
Dr Adam Kucharski, from the London School of Hygiene & Tropical Medicine, said: ‘An important finding of our study was that moderate social distancing, combined with contact tracing and testing, could help control the spread of Covid-19 while reducing the number of people who need to be in quarantine.’
Dr Firth, from Oxford’s Department of Zoology, said: ‘Going forward, real-world networks will be an important tool for considering epidemic spread, and our study provides new evidence into how contact tracing and social distancing strategies could potentially be combined to control Covid-19 outbreaks within local populations.
‘It is important to note the limitations of this study and the current state of knowledge – particularly that the social network here is taken from within a single, small town, and therefore larger-scale tracking efforts will be needed if we want to extrapolate these approaches to larger cities.’
Due to the rapid response nature of this research, it has not yet been peer-reviewed, but it provides valuable insight into potential ways to contain the pandemic using data from real-world social interactions – something which has not been done in such detail until now.
The authors have also built an interactive web app to accompany the paper, which can be accessed at https://biouea.shinyapps.io/covidhm_shiny/.