A team at Oxford University is sharing an epidemiological model to help configure a contact tracing app for coronavirus. The model offers several safe configurations to introduce an app and a framework to optimise the app after it is released.
The simulations confirm that if around half the total population use the app, alongside other interventions, it has the potential to stop the epidemic and help to keep countries out of lockdown. These research efforts are supporting several European projects including the UK’s national programme led by NHSX, a joint unit comprised of teams from NHS England and the Department of Health & Social Care.
After analysis of the transmission dynamics of the early coronavirus epidemic in China, the Oxford team demonstrated that almost half of all transmissions occurred before anyone showed symptoms. They also estimated that delaying contact tracing by even a day from the onset of symptoms could make the difference between epidemic control and coronavirus resurgence. To respond to this, the team rapidly conceptualised a simple mobile contact tracing app to help control the spread of coronavirus, save lives and ease the population out of lockdown.
Professor Christophe Fraser, senior author of the latest report from Oxford University’s Nuffield Department of Medicine explains, “We’ve simulated coronavirus in a model city of 1 million inhabitants with a wide range of realistic epidemiological configurations to explore options for controlling transmission. Our results suggest a digital contact tracing app, if carefully implemented alongside other measures, has the potential to substantially reduce the number of new coronavirus cases, hospitalisations and ICU admissions. Our models show we can stop the epidemic if approximately 60% of the population use the app, and even with lower numbers of app users, we still estimate a reduction in the number of coronavirus cases and deaths.”
The Oxford team hope these latest simulations will keep digital contact tracing efforts focussed on core epidemiological principles, whichever technical solution is used. The different configurations of the model explore different options for quarantining contacts and offer solutions for contact tracing with different levels of testing available. The model can also be adjusted to account for different speeds of epidemic growth, ensuring it can robustly respond to different predictions of epidemic progression in this rapidly evolving epidemic. Currently, the simulations are based on UK demographics and usage of mobile phones, but the model can be easily adjusted to simulate coronavirus epidemics in other countries.
Professor Fraser adds, 'By openly sharing our models and our algorithm we are providing governments and health services with the epidemiological tools to compare and evaluate different strategies for contact tracing alongside other epidemic control approaches (see Figure 1). Enabling all countries to consider optimising the app’s epidemiological settings before and after launch will help to ensure countries make the greatest possible contribution towards controlling the epidemic.'
This latest work contributes to the development of the strategy around the role of testing in contact tracing and contact isolation. Dr David Bonsall, co-lead on the project and senior researcher at Oxford’s Nuffield Department of Medicine and clinician at the John Radcliffe Hospital, confirms, 'Initiating contact tracing based on symptoms makes sense epidemiologically because it’s fast enough to reach people before they transmit. Our simulations predict loss of epidemic control when tracing is delayed to wait for test results, and overall results in more deaths, and more people in quarantine. You achieve the best of both worlds when virological tests are used to follow-up and promptly release people. With the right configuration, we can all use the technology to save lives and help to protect vulnerable groups.'
Professor Michael Parker, a senior ethicist at Oxford University’s Nuffield Department of Population Health, co-author and advisor to the mobile app project team explains, 'In addition to acknowledging the importance of saving lives and reducing suffering, we have rightly heard concerns relating to the potential misuse of data for digital contact tracing solutions. It is reassuring to know that these concerns are being addressed by NHSX through independent ethical oversight, and extensive stakeholder consultation at every stage of the app development. Each individual who chooses to install this technology needs to feel confident that these issues have been taken seriously. This can provide the reassurance we all need to enable us to install the app, knowing that our contact record and anonymous alerts could help save lives and our health services, and prevent a future resurgence. Over time we could see that our combined efforts help us to avoid repeated and devastating lockdowns.'
The Oxford team also understands that multiple public fora have been set up by NHSX to allow questions from key stakeholder groups and survey potential users to gather critical feedback to inform the proposed plans. Initial surveys carried out by a team of behavioural economists collaborating with the team at Oxford University have contributed another stream of feedback from over 6000 potential app users in 5 countries, which suggest that 73.6% of users would be likely to install a contact tracing app for coronavirus in the UK, and between 67.5% - 85.5% in France, Germany, Italy and the USA.
Professor Fraser concludes, 'We need strategies to exit from the lockdown whilst minimising the risk of resurgence. Combined with other interventions such as community testing and continued shielding of vulnerable individuals, digital contact tracing can help prevent coronavirus from rapidly re-emerging. We hope these latest findings will provide valuable evidence that mobile contact tracing can be carefully deployed after consideration of key epidemiological parameters, combined with critical ethical principles, to ensure we can save lives, reduce the number of people who need to remain in self-isolation, and support as many people as possible to safely and responsibly start returning to active life again. Our models show we can stop the epidemic if approximately 60% of the whole population use the app and adhere to the app’s recommendations. Lower numbers of app users will also have a positive effect; we estimate that one infection will be averted for every one to two users.'
The full details of the models and simulations are available in our report. The algorithm will also be available open source soon. For further information, please contact Andrea Stewart, Communications Lead - UK time zone. WhatsApp +44 7528 132 489 and email: andrea.stewart@bdi.ox.ac.uk.
Visit our website: www.coronavirus-fraser-group.org
Questions you might have?
How does the app work?
Questions relating to the configuration are best directed to NHSX who are developing the app. It is understood that NHSX are considering some of the following specifications. A person installs the app which starts to record close proximity contacts with other app users. A person develops symptoms and reports these symptoms through self-diagnosis. This person is asked to self-isolate with their household. Their close proximity contacts are anonymously notified, advised to self-isolate, and given further recommendations.
What do the user uptake numbers mean?
Our models suggest we need around 56% of the total population to use the app to completely suppress the epidemic, if combined with ‘shielding’ of over 70s (see below). Usage requirement may be lower if the app is used in conjunction with further social distancing interventions. Lower usage has the effect of slowing resurgence and potentially delaying the start of a second lock down.
How does testing relate to the app?
Using self-diagnosis, based on a series of questions, will rapidly trigger notifications encouraging other people to isolate before they become infectious. Testing app users after they self-report could ensure the quick release of contacts if the test is negative. Starting contact tracing only after a positive test is less effective at suppressing the epidemic, as crucial time is lost during which contacts are already infectious. If testing can be scaled up and sped up, it could be a valuable addition to the digital contact tracing process.
How are the elderly accounted for with smartphone usage?
Using the latest data on smartphone usage by age, sourced from the UK’s communications regulator OFCOM, the model does not include over 70s in the contact tracing process due to low smartphone usage on average. This vulnerable group will still benefit from protection if they are quarantined, in accordance with UK policy on ‘shielding’. Even people not using the app will receive some protection from its effect, through decreased transmission in the community.
What if not many people use the app?
Our simulations suggest a reduction in the number of coronavirus cases and deaths even with low numbers. We estimate that we prevent approximately one infection for every one or two users of the app. Moderate uptake will also still result in delaying the need for a second lockdown. The app is a tool for anonymously and instantaneously communicating information from infected persons to recent close proximity contacts. The effectiveness of the policy in controlling the epidemic is dependent on users responding to the guidance; the app requires a public health campaign that encourages appropriate use and response, and will need to build trust from users in the effectiveness of the system.
What are some of the scenarios for configuring the app?
See Figure 1. Enlarge image to view full details. Dr Rob Hinch, senior researcher at Oxford University’s Nuffield Department of Medicine, “Our simulation results present five key scenarios with various amounts of contact tracing and strategies for releasing people from quarantine, which we compare to a simulation of the epidemic with no contact tracing at all. Our mathematical model allows governments and app developers to compare different app configurations, exploring how each impacts differently on the epidemic.”
Figure 1. Enlarge image to view full details. Credit: Fraser Group, Oxford University’s Big Data Institute, Nuffield Department of Medicine. The icons used are taken from Font Awesome and licensed under the Creative Commons Attribution 4.0 International license.
Credit: Fraser Group, Oxford University’s Big Data InstituteCredit: Fraser Group, Oxford University’s Big Data Institute
