An international research scientist consortium has developed a COVID Symptom Tracker, already in use by more than 2.5m people in the US and UK, to generate data on hot spots and inform self-isolation decisions.
A consortium of scientists with expertise in big data research and epidemiology recently developed a COVID Symptom Tracker app aimed at rapidly collecting information to aid in the response to the ongoing COVID-19 pandemic. Early use of the app by more than 2.5m people in the US and the UK has generated valuable data about COVID-19 for physicians, scientists, and public officials to better fight the viral outbreak.
"The app collects daily information from individuals in the community about whether they feel well, and if not, their specific symptoms and if they have been tested for COVID-19," said senior author Dr Andrew T Chan, chief of the clinical and translational epidemiology unit at Massachusetts General Hospital (MGH) and director of cancer epidemiology at the MGH Cancer Centre. The app is designed to provide insights on where the COVID-19 hot spots are and new symptoms to look out for, and it may be useful as a planning tool to inform guidelines around self-isolation, identify regions in need of additional ventilators and expanded hospital capacity, and provide real-time data to prepare for future outbreaks.
The COVID Symptom Tracker was launched in the UK on 24 March and became available in the US on 29 March. Since launch, it has been used by more than 3m people. "This work has led to the development of accurate models of COVID-19 infection rates in the absence of sufficient population testing," said Chan. "For example, the UK government has acted upon these estimates by providing advanced notice to local health authorities about when to expect a surge of cases." Researchers are also using results from the app to investigate risk factors for infection, as well as the effects of COVID-19 on patients' health.
Chan also pointed out that the app does not have any contact tracing function in contrast with software that is being rolled out in the future by some states in collaboration with Apple and Google. "Our app is designed to be entirely voluntary so that they can share information about how they are feeling in a way that safeguards their privacy."
The team is asking individuals, even those who are feeling well, to download the app and participate in this effort to provide critically valuable information related to COVID-19. The study was conducted by a team led by researchers at Massachusetts General Hospital (MGH), King's College London, and Zoe Global Ltd.
Abstract
The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic (COVID-19) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) consortium to bring together scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application that we launched in the UK on March 24, 2020 and the US on March 29, 2020 garnering more than 2.8 million users as of May 2, 2020. This mobile application offers data on risk factors, herald symptoms, clinical outcomes, and geographical hot spots. This initiative offers critical proof-of-concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis which is critical for a data-driven response to this public health challenge.
Authors
David A Drew, Long H Nguyen, Claire J Steves, Cristina Menni, Maxim Freydin, Thomas Varsavsky, Carole H Sudre, M Jorge Cardoso, Sebastien Ourselin, Jonathan Wolf, Tim D Spector, Andrew T Chan, COPE Consortium
The researchers at King's College London, Massachusetts General Hospital and health science company ZOE have also developed an artificial intelligence diagnostic that can predict whether someone is likely to have COVID-19 based on their symptoms.
The AI model uses data from the COVID Symptom Study app to predict COVID-19 infection, by comparing people's symptoms and the results of traditional COVID tests. Researchers say this may provide help for populations where access to testing is limited. Two clinical trials in the UK and the US are due to start shortly.
More than 3.3m people globally have downloaded the app and are using it to report daily on their health status, whether they feel well or have any new symptoms such as persistent cough, fever, fatigue and loss of taste or smell (anosmia).
In this study, the researchers analysed data gathered from just under 2.5m people in the UK and US who had been regularly logging their health status in the app, around a third of whom had logged symptoms associated with COVID-19. Of these, 18,374 reported having had a test for coronavirus, with 7,178 people testing positive.
The research team investigated which symptoms known to be associated with COVID-19 were most likely to be associated with a positive test. They found a wide range of symptoms compared to cold and flu, and warn against focusing only on fever and cough. Indeed, they found loss of taste and smell (anosmia) was particularly striking, with two thirds of users testing positive for coronavirus infection reporting this symptom compared with just over a fifth of the participants who tested negative.
The findings suggest that anosmia is a stronger predictor of COVID-19 than fever, supporting anecdotal reports of loss of smell and taste as a common symptom of the disease.
The researchers then created a mathematical model that predicted with nearly 80% accuracy whether an individual is likely to have COVID-19 based on their age, sex and a combination of four key symptoms: loss of smell or taste, severe or persistent cough, fatigue and skipping meals. Applying this model to the entire group of over 800,000 app users experiencing symptoms predicted that just under a fifth of those who were unwell (17.42%) were likely to have COVID-19 at that time.
Researchers suggest that combining this AI prediction with widespread adoption of the app could help to identify those who are likely to be infectious as soon as the earliest symptoms start to appear, focusing tracking and testing efforts where they are most needed.
Professor Tim Spector from King's College London said: "Our results suggest that loss of taste or smell is a key early warning sign of COVID-19 infection and should be included in routine screening for the disease. We strongly urge governments and health authorities everywhere to make this information more widely known, and advise anyone experiencing sudden loss of smell or taste to assume that they are infected and follow local self-isolation guidelines."
Abstract
A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.
Authors
Cristina Menni, Ana M Valdes, Maxim B Freidin, Carole H Sudre, Long H Nguyen, David A Drew, Sajaysurya Ganesh, Thomas Varsavsky, M Jorge Cardoso, Julia S El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth CE Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Sebastien Ourselin, Andrew T Chan, Claire J Steves, Tim D Spect
[link url="https://www.sciencedaily.com/releases/2020/05/200505105325.htm"]Massachusetts General Hospital material[/link]
[link url="https://science.sciencemag.org/content/early/2020/05/05/science.abc0473"]Science abstract[/link]
[link url="https://www.kcl.ac.uk/news/new-ai-diagnostic-can-predict-covid-19-without-testing"]King’s College London material[/link]
[link url="https://www.nature.com/articles/s41591-020-0916-2"]Nature Medicine abstract[/link]