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Rapid implementation of mobile technology for real-time epidemiology of COVID-19

Our take —

Preliminary results from this study of real-time COVID-19 symptom data collected through an app suggests that app-based symptom tracking may be useful for predicting spikes and drops in new cases several days in advance of other more traditional measures (e.g. confirmed positive tests).

Study design

Cross-Sectional; Modeling/Simulation; Other

Study population and setting

The authors developed an app-based symptom tracker, “COVID Symptom Study” (referred to previously as COVID Symptom Tracker), and demonstrated proof-of-concept for real-time tracking of symptoms through mobile phones. App data can be used to immediately inform public health responses to COVID-19 by detecting outbreaks, disparities in testing, and emerging symptoms. The app was tested in the United Kingdom and United States of America. Recruitment for the app relied on downloads, but also recruitment through existing large cohort studies that often have higher under-represented populations.

Summary of Main Findings

Initial findings were reported based on 1.6 million users in the UK, including 265,851 who experienced COVID-19 symptoms, of whom 0.4% received testing. Those that reported fatigue and/or cough along with another symptoms were more likely to test positive, but 20% of individuals that reported this combination of symptoms did not receive testing. Among those who tested positive, loss of smell was more common than fever, suggesting that anosmia may be a good predictor for testing positive for COVID-19. Based on modeling using existing data, the authors were able to predict increases and decreases in COVID-19 cases in geographic areas across the UK several days in advance of confirmed case data.

Study Strengths

The app provides a useful tool to collect longitudinal data on COVID-19 symptoms and testing on the scale needed for meaningful analysis. Recruitment for app use included leveraging existing large cohort studies, thereby improving diversity and the potential to link existing cohort data with COVID Symptom Study data. Software updates allow questions to be modified as knowledge of the outbreak develops and new hypotheses emerge. As the app collected data on symptoms and testing over time, authors were able to assess which symptoms were more likely to result in a positive test and predict where hotspots may emerge.

Limitations

As the authors acknowledge, the population contributing data to the COVID Symptom Study may not be representative of the broader population. App users must be over the age of 18 years, and app use requires daily access to a smartphone as well as English language skills. The vast majority (75%) of the first 1.6 million users in the UK were female which may bias study findings, especially as male sex is associated with COVID-19 disease severity. The app does not currently meet accessibility standards for those with limited sight.

Value added

This app demonstrates proof-of-concept for large scale real-time mobile data collection on symptoms, testing, and mobility data of potential cases that can be used to predict potential outbreaks.