By Professor Ganna Pogrebna, The Department of Economics, Professor of Behavioral Economics and Data Science
Could a new data model, designed specifically to understand the risks and susceptibility of vulnerable people to COVID-19, help us better understand how they can exit lockdown in a way that still mitigates the risks of the virus? Our new study finds that decreasing the exposure of this group by as little as 10% from now, could save 3,681 lives. It explores how practical options and technological interventions, from Bluetooth wristbands to proximity sensors, can help to achieve that alongside effective incentives to motivate people.
The COVID-19 pandemic has changed the life of every individual on the planet. These days, many people look to the behavioural scientists for solutions as in the absence of the vaccine behavioural measures (physical distancing, facial mask wearing, etc.) are the only tangible (though, no doubt, temporary) means to decrease the virus spread.
Contact-tracing, which has been used alongside other protection measures across the world to help achieve behavioural change, can keep a record of any new infection cases and anyone who has been close to them. Many contact-tracing app solutions have been developed in recent months in the UK and worldwide. This could enable uninfected and immune people (e.g., people with the so-called “hidden immunity”) to leave their homes, while people who might have been infected instructed to self-isolate. Bluetooth technology has been used previously for messaging and tracking of nearby devices using proximity detection. Apple and Google along with many health authorities have proposed software smartphone hosted apps using Bluetooth Low Energy (BLE) to automate the contact tracing process. BLE is a form of wireless communication designed especially for short-range communication suitable for situations where battery life is preferred over high data transfer speeds.
While an important part of the fight against COVID-19, a contract-tracing app is realistically not a viable option for everyone. For instance, vulnerable and older people are more likely to use older smartphones without the BLE feature. Other vulnerable groups (specifically, those who have had an organ transplant and are on immune suppressive therapy; have had a bone marrow transplant in the last 24 months; are on immune suppressive therapy for graft versus host disease; have blood cancer such as leukaemia, lymphoma or myelodysplastic syndrome; are having chemotherapy or radiotherapy; chronic kidney failure; heart disease; chronic lung disease; a non-haematological cancer; diabetes, obesity, chronic liver disease, neurological conditions such as stroke or dementia, etc.) might not see the direct benefit of downloading and using the app as they do not understand the underlying incentives behind contact tracing and how it can benefit them.
Long established compartmental epidemiological models like the Susceptible-Infected-Removed (SIR) model and the Susceptible – Exposed – Infectious – Recovered (SEIR) model do not account for the variability encountered in the severity of the COVID-19 disease across different population groups. In a way, they are designed to provide “one-size-fits-all” solutions. Yet, under circumstances when some populational groups are a lot more vulnerable to the virus than others (elderly, people with pre-existing health conditions, etc.), these groups may need extra help and more customised solutions to help them decrease or avoid the risk of being infected.
To overcome the limitation of the current models, our Behavioural Data Science Turing Special Interest Group, together with researchers from the Nottingham Trent University, University College London, and the University of Sydney published a study in Sensors, proposing a new model – Susceptible – Exposed – Infectious – Recovered for the vulnerable (SEIR-v) model – through which the population could be segmented into groups according to their vulnerability to coronavirus.
This segmentation enables the analysis of the epidemic’s spread when different contention measures are applied to different groups in the society. These measures depend on their vulnerability to the disease. Simulated results show that such approach can save lives. Using this novel epidemiological as well as behavioural segmentation, we propose three possible solutions to support vulnerable people: (i) contact tracing wristband or wearable; (ii) social distancing alert mechanism; and (iii) a wearable to monitor symptoms. The real-world testing of these wearable solutions has already started in Nottingham under the leadership of Dr Eiman Kanjo from the Nottingham Trent University.
This project shows how behavioural science can help suggest effective incentive-compatible mechanisms for “smarter” adoption. Specifically, incentives may be more effective if they appeal to people’s “present bias,” which is the tendency to pursue smaller and more immediate rewards rather than bigger and more abstract goals, such “eradicating COVID-19 in the world.” Considering that vulnerable populations generally tend to procure (health)care services more often, the function of contact tracing is likely to succeed if it is embedded into the general care applications and services. For example, framing the contact tracing technology as a “digital nurse” for the vulnerable groups, that aims to monitor (in real time) the state of the wearer and potentially offer some desirable care features (e.g., health monitoring, automated calls for medical help, etc.)
Specifically, incentives may be more effective if they appeal to people’s “present bias,” which is the tendency to pursue smaller and more immediate rewards rather than bigger and more abstract goals, such “eradicating COVID-19 in the world.”
In circumstances when much uncertainty exists about the characteristics of COVID-19, we show how a reduction in the exposure of vulnerable individuals can minimise the number of deaths caused by the disease, using the UK as a case study. With over 10 million downloads of the NHS COVID-19 app already reported it is clear that the effectiveness of contact tracing hinges on how many people use it (and self-isolate if they are notified that they have been in contact with someone who has the virus). It is proposed that governments could provide vulnerable individuals with a BLE wristband, capturing populations usually missed by systems relying on smartphones and an app only. Having a clear value proposition, which would go beyond the functional purpose of tracing would allow the technological solutions to succeed with the elderly population more easily, as users will see not only how their data can benefit society, but also how their data can help them receive better, more efficient, and higher quality care.
- More about Professor Ganna Pogrebna at the University of Birmingham
- This blog was originally posted on The Alan Turing Institute
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The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of the University of Birmingham.