Novel methods in the surveillance of influenza-like illness (ILI): exploration of data from the symptom assessment app Ada, Germany observational case study. (Preprint)

BACKGROUND The free app Ada allows users to enter symptoms they are experiencing, and applies a probabilistic reasoning model to provide a list of possible causes for those symptoms. OBJECTIVE The objective of our study was to explore the potential contribution of Ada data to syndromic surveillance, by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. METHODS We extracted data for all assessments performed by Ada users in Germany over three seasons (2017/18, 2018/19 and 2019/20), and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age-group), standardised for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson’s correlation coefficient. RESULTS In total 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age-groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson’s correlations: 2017-18: r = 0.86 (95% CI 0.76-0.92), P<.0001; 2018-19: r = 0.90 (95% CI 0.84-0.94), P <.0001; 2019-20: r = 0.64 (95% CI 0.44-0.78), P <.0001). However, there were differences in the exact timing and nature of the epidemic curves between years. CONCLUSIONS With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems, or to the syndromic surveillance of symptoms not covered by existing systems.