Track and trace apps used to reduce the spread of Covid-19 are unlikely to be effective without proper uptake and alongside other public health control measures, finds a new study by UCL researchers.

The systematic review, published in Lancet Digital Health, shows that evidence around the effectiveness of automated contact tracing systems is currently very limited, and is likely to be required in conjunction with automated approaches such as physical distancing and closure of indoor spaces.

Researcher reviewed more than 4,000 papers on automated and partially-automated contact tracing and analysed 15 relevant studies to understand the potential impact these tools could have in controlling the Covid-19 pandemic.

Lead author Dr Isobel Braithwaite, UCL Institute of Health Informatics, said: “Across a number of modelling studies, we found a consistent picture that although automated contact tracing could support manual contact tracing, the systems will require large-scale uptake by the population and strict adherence to quarantine advice by contacts notified to have a significant impact on reducing transmission.”

The authors suggest that even under optimistic assumptions – where 75-80% of UK smartphone owners are using a contact tracing app, and 90-100% of identified potential close contacts initially adhere to quarantine advice – automated contact tracing methods would still need to be used within an integrated public health response to prevent exponential growth of the epidemic.

High population uptake of relevant apps is required alongside other control measures

In total, 4,033 papers published between 1 Jan 2000 and 14 April 2020 were reviewed, which allowed researchers to identify 15 papers with useful data. The seven studies that addressed automated contact tracing directly were modelling studies that all focused on Covid-19. Five studies of partially-automated contact tracing were descriptive observational studies or case studies, and three studies of automated contact detection looked at a similar disease context to Covid-19, but did not include subsequent tracing or contact notification.

Partially-automated systems may have some automated processes, for instance in determining the duration of follow-up of contacts required, but do not use proximity of smartphones as a proxy for contact with an infected person.

Analysis of automated contact tracing apps generally suggested that high population uptake of relevant apps is required alongside other control measures, while partially-automated systems often had better follow-up and slightly more timely intervention.

Contact tracing apps raise potential privacy and ethics concerns

Dr Robert Aldridge, UCL Institute of Health Informaticsa, dded: “We currently do not have good evidence about whether a notification from a smartphone app is as effective in breaking chains of transmission by giving advice to isolate due to contact with a case of Covid-19 when compared to advice provided by a public health contact tracer. We urgently need to study this evidence gap and examine how automated approaches can be integrated with existing contact tracing and disease control strategies, and generate evidence on whether these new digital approaches are cost-effective and equitable.”

If implemented effectively and quarantine advice is adhered to appropriately, automated contact tracing may offer benefits such as reducing reliance on human recall of close contacts, which could enable identification of additional at-risk individuals, informing potentially affected people in real-time, and saving on resources.

The researchers also said that automated approaches raise potential privacy and ethics concerns, and also rely on high smartphone ownership, so they may be of very limited value in some countries. Too much reliance on automated contact tracing apps may also increase the risk of Covid-19 for vulnerable and digitally-excluded groups such as older people and people experiencing homelessness.

This study is co-authored by researchers UCL Public Health Data Science Research Group, Institute of Health Informatics, Department of Applied Health Research, and Collaborative Centre for Inclusion Health.