Syndromic surveillance insights from a symptom assessment app before and during COVID-19 measures in Germany and the United Kingdom: results from repeated cross-sectional analyses

Abstract

Background: Unprecedented lockdown measures have been introduced in countries across the world to mitigate the spread and consequences of COVID-19. While attention has focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to limitations of existing syndromic surveillance data and tools. Objective: To explore the added value of mobile phone app-based symptom assessment tools as real time health insight providers to inform public health policy makers. Methods: A comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an Ada assessment in Germany and the United Kingdom (UK) was conducted between two periods: before and after the implementation of Phase One COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using Pearson's Chi-squared test and reported as Log2 Fold Changes (Log2 FC). Results: Between 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Between 34,200-37,400 symptomatic users in the UK reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany 68,600/103,200, 66.52%; UK 51,200/71,600, 72.74%). The majority (Germany 68,500/100,000, 68.45%; UK 50,900/68,800, 73.91%) were aged between 10 and 29 years, and about a quarter (Germany 26,200/100,000, 26.15%; UK 14,900/68,800, 21.65%) were between 30-59 years. 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures as compared to the Baseline period, and 34 of these were found in both countries. The following mental health symptoms (Log2 FC, P-value) were reported less often during the Measures period: inability to manage constant stress and demands at work (-1.07, P<.001), memory difficulty (-0.56, P<.001), depressed mood (-0.42, P<.001), and impaired concentration (-0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. Fourteen of the 34 symptoms had statistically significant associations with weather variables. Conclusions: Symptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

The Diagnostic Efficacy of an App-based Diagnostic Health Care Application in the Emergency Room: eRadaR-Trial. A prospective, Double-blinded, Observational Study

“…CDSS facilitating clinical decision-making have exhibited strong performance in several previous studies, including the Ada-App 8,9,19–23. Gilbert et al10 recently evaluated the performance of eight digital symptom assessment apps, showing that Ada achieved a higher accuracy (71%) in comparison to other apps for the top 3 diagnoses (average: 47%), providing safe urgency advice in 97%.…”

The Use of a Self-triage Tool to Predict COVID-19 Cases and Hospitalizations in the State of Georgia

“…4 Studies in Europe and Asia have found that self-reported symptoms collected through mobile applications had strong spatial correlations with confirmed COVID-19 cases 5 and that by collecting data before and after COVID-19 restrictions, the tool was effective in evaluating control measures. 6 In April 2020, C19Check.com (C19Check) was launched by Emory University and Vital Software Inc. in Atlanta, Georgia (GA). The online symptom tracker, freely available in 31 languages, prompts users to report their symptoms and then generates evidence-based summaries of risk of COVID-19 infection and advice for seeking healthcare.…”

“…The amount of error in the forecast is likely because C19Check use itself is not a cause of the surges and declines in cases and hospitalizations. The significant results for all time-lags tested indicate that our findings on the predictability of C19Check use were not impacted by time-lags, demonstrating the effectiveness of C19Check as a tool for syndromic surveillance of COVID-19 cases and hospitalizations in GA. Other real-time syndromic surveillance tools have been used to detect early signals, monitor population transmission dynamics and identify hotspots in different countries 5,6,9,10 and various regions of the US. 9,11 However, we also evaluated the performance of an internet-based self-triage tool in predicting COVID-19 cases and hospitalizations.…”

Participatory syndromic surveillance as a tool for tracking COVID-19 in Bangladesh

“…While participatory crowdsourced syndromic surveillance has been utilized in many contexts [1]–[7], including for COVID-19 [8], [9], their ability to track an emerging outbreak at a high spatial resolution has not been evaluated previously. Here, we show that one such system, though noisy, provides an indication of where and when to expect new cases, suggesting that it could be a useful model in other places that need to map COVID-19 risk for decision making.…”