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U.K. Study Shows Power of Digital Contact Tracing for COVID-19

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COVID-19 cases in the United Kindom. Hands hold a smart phone with the NHS COVID-19 app
Credit: Adapted from Getty Image and Wymant C, Nature, 2021

There’s been much interest in using digital technology to help contain the spread of COVID-19 in our communities. The idea is to make available opt-in smart phone apps that create a log of other apps operating on the phones of nearby participants. If a participant tests positive for COVID-19 and enters the result, the app will then send automatic alerts to those phones—and participants—who recently came into close proximity with them.

In theory, digital tracing would be much faster and more efficient than the challenging detective work involved in traditional contract tracing. But many have wondered how well such an opt-in system would work in practice. A recent paper, published in the journal Nature, shows that a COVID-19 digital tracing app worked quite well in the United Kingdom [1].

The research comes from Christophe Fraser, Oxford University, and his colleagues in the U.K. The team studied the NHS COVID-19 app, the National Health Service’s digital tracing smart phone app for England and Wales. Launched in September 2020, the app has been downloaded onto 21 million devices and used regularly by about half of eligible smart phone users, ages 16 and older. That’s 16.5 million of 33.7 million people, or more than a quarter of the total population of England and Wales.

From the end of September through December 2020, the app sent about 1.7 million exposure notifications. That’s 4.4 on average for every person with COVID-19 who opted-in to the digital tracing app.

The researchers estimate that around 6 percent of app users who received notifications of close contact with a positive case went on to test positive themselves. That’s similar to what’s been observed in traditional contact tracing.

Next, they used two different approaches to construct mathematical and statistical models to determine how likely it was that a notified contact, if infected, would quarantine in a timely manner. Though the two approaches arrived at somewhat different answers, their combined outputs suggest that the app may have stopped anywhere from 200,000 to 900,000 infections in just three months. This means that roughly one case was averted for each COVID-19 case that consented to having their contacts notified through the app.

Of course, these apps are only as good as the total number of people who download and use them faithfully. They estimate that for every 1 percent increase in app users, the number of COVID-19 cases could be reduced by another 1 or 2 percent. While those numbers might sound small, they can be quite significant when one considers the devastating impact that COVID-19 continues to have on the lives and livelihoods of people all around the world.

Reference:

[1] The epidemiological impact of the NHS COVID-19 App. Wymant C, Ferretti L, Tsallis D, Charalambides M, Abeler-Dörner L, Bonsall D, Hinch R, Kendall M, Milsom L, Ayres M, Holmes C, Briers M, Fraser C. Nature. 2021 May 12.

Links:

COVID-19 Research (NIH)

NHS COVID-19 App

Christophe Fraser (Oxford University, UK)


Predicting ‘Long COVID Syndrome’ with Help of a Smartphone App

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Zoe COVID Sympton Study Tracker app
Credit: Zoe Global

As devastating as this pandemic has been, it’s truly inspiring to see the many innovative ways in which researchers around the world have enlisted the help of everyday citizens to beat COVID-19. An intriguing example is the COVID Symptom Study’s smartphone-based app, which already has been downloaded millions of times, mostly in the United States and United Kingdom. Analyzing data from 2.6 million app users, researchers published a paper last summer showing that self-reported symptoms can help to predict infection with SARS-CoV-2, the coronavirus that causes COVID-19 [1].

New work from the COVID Symptom Study now takes advantage of the smartphone app to shed more light on Long COVID Syndrome [2], in which people experience a constellation of symptoms long past the time that they’ve recovered from the initial stages of COVID-19 illness. Such symptoms, which can include fatigue, shortness of breath, “brain fog,” sleep disorders, fevers, gastrointestinal symptoms, anxiety, and depression, can persist for months and can range from mild to incapacitating

This latest findings, published in the journal Nature Medicine, come from a team led by Claire Steves and Tim Spector, King’s College London, and their colleagues, and that includes NIH grantee Andrew Chan, Massachusetts General Hospital, Boston, and others supported by the Massachusetts Consortium on Pathogen Readiness. The team began by looking at data recorded between March 24-Sept. 2, 2020 from about 4.2 million app users with an average age of 45, about 90 percent of whom lived in the U.K., with smaller numbers from the U.S. and Sweden.

For this particular study, the researchers decided to focused on 4,182 app users, all with confirmed COVID-19, who had consistently logged in their symptoms. Because these individuals also started using the app when they still felt physically well, the researchers could assess their COVID-19-associated symptoms over the course of the illness.

While most people who developed COVID-19 were back to normal in less than two weeks, the data suggest that one in 20 people with COVID-19 are likely to suffer symptoms of Long COVID that persist for eight weeks or more. About one in 50 people continued to have symptoms for 12 weeks or more. That suggests Long COVID could potentially affect many hundreds of thousands of people in the U.K. alone and millions more worldwide.

The team found that the individuals most likely to develop Long COVID were older people, women, and especially those who experienced five or more symptoms. The nature and order of symptoms, which included fatigue, headache, shortness of breath, and loss of smell, didn’t matter. People with asthma also were more likely to develop long-lasting symptoms, although the study found no clear links to any other pre-existing health conditions.

Using this information, the researchers developed a model to predict which individuals were most likely to develop Long COVID. Remarkably, this simple algorithm—based on age, gender, and number of early symptoms–accurately predicted almost 70 percent of cases of Long COVID. It was also about 70 percent effective in avoiding false alarms.

The team also validated the algorithm’s predictive ability in data from an independent group of 2,472 people with confirmed COVID-19 and a range of symptoms. In this group, having more than five symptoms within the first week also proved to be the strongest predictor of Long COVID. And, again, the model worked quite well in identifying those most likely to develop Long COVID.

These findings come as yet another important reminder of the profound impact of the COVID-19 pandemic on public health. This includes not only people who are hospitalized with severe COVID-19 but, all too often, those who get through the initial period of infection relatively unscathed.

Recently, NIH announced a $1.15 billion investment to identify the causes of Long COVID, to develop ways of treating individuals who don’t fully recover, and, ultimately, to prevent the disorder. We’ve been working diligently in recent weeks to identify the most pressing questions and areas of greatest opportunity to address this growing public health threat. As a first step, NIH is funding an effort to track the recovery paths of at least 40,000 adults and children infected with SARS-CoV-2, to learn more about who develops long-term effects and who doesn’t. If you’d like to find a way to pitch in and help, getting involved in the COVID Symptom Study is as easy as downloading the app.

References:

[1] Real-time tracking of self-reported symptoms to predict potential COVID-19. Menni C, Valdes AM, Freidin MB, Sudre CH, Nguyen LH, Drew DA, Ganesh S, Varsavsky T, Cardoso MJ, El-Sayed Moustafa JS, Visconti A, Hysi P, Bowyer RCE, Mangino M, Falchi M, Wolf J, Ourselin S, Chan AT, Steves CJ, Spector TD. Nat Med. 2020 Jul;26(7):1037-1040. doi: 10.1038/s41591-020-0916-2. Epub 2020 May 11.

[2] Attributes and predictors of long COVID. Sudre CH, Murray B, Varsavsky T, Graham MS, Penfold RS, Bowyer RC, Pujol JC, Klaser K, Antonelli M, Canas LS, Molteni E, Modat M, Jorge Cardoso M, May A, Ganesh S, Davies R, Nguyen LH, Drew DA, Astley CM, Joshi AD, Merino J, Tsereteli N, Fall T, Gomez MF, Duncan EL, Menni C, Williams FMK, Franks PW, Chan AT, Wolf J, Ourselin S, Spector T, Steves CJ. Nat Med. 2021 Mar 10.

Links:

NIH launches new initiative to study to “Long COVID”. 2021 Feb 23. (NIH)

COVID-19 Research (NIH)

Massachusetts Consortium on Pathogen Readiness (Boston)

COVID Symptom Study

Claire Steves (King’s College London, United Kingdom)

Tim Spector (King’s College London)

Andrew Chan (Massachusetts General Hospital, Boston)

NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases