Abstract
IntroductionParticipatory surveillance systems provide rich crowdsourced data, profiling individuals and their health status at a given time. We explored the usefulness of data from GrippeNet.fr, a participatory surveillance system, to estimate influenza-related illness incidence in France. Methods: GrippeNet.fr is an online cohort since 2012 averaging ca. 5,000 weekly participants reporting signs/symptoms suggestive of influenza. GrippeNet.fr has flexible criteria to define influenza-related illness. Different case definitions based on reported signs/symptoms and inclusions of criteria accounting for individuals’ reporting and participation were used to produce influenza-related illness incidence estimates, which were compared to those from sentinel networks. We focused on the 2012/13 and 2013/14 seasons when two sentinel networks, monitoring influenza-like-illness (ILI) and acute respiratory infections (ARI) existed in France. Results: GrippeNet.fr incidence estimates agreed well with official temporal trends, with a higher accuracy for ARI than ILI. The influenza epidemic peak was often anticipated by one week, despite irregular participation of individuals. The European Centre for Disease Prevention and Control ILI definition, commonly used by participatory surveillance in Europe, performed better in tracking ARI than ILI when applied to GrippeNet.fr data. Conclusion: Evaluation of the epidemic intensity from crowdsourced data requires epidemic and intensity threshold estimations from several consecutive seasons. The study provides a standardised analytical framework for crowdsourced surveillance showing high sensitivity in detecting influenza-related changes in the population. It contributes to improve the comparability of epidemics across seasons and with sentinel systems. In France, GrippeNet.fr may supplement the ILI sentinel network after ARI surveillance discontinuation in 2014.
Highlights
Participatory surveillance systems provide rich crowdsourced data, profiling individuals and their health status at a given time
Errors were considerably reduced in both seasons and for both indicators. This improvement is obtained as the first survey exclusion removes the large epidemic peak observed at the beginning of 2012/13 season that is not reported by sentinel sources, as illustrated in Figure 1 for the particular examples of incidence estimates based on the GNILI– and GNECDC ILI definitions
We evaluated the accuracy of seven influenza case definitions and of different inclusion criteria accounting for variable reporting and participating behaviours of individuals in the online cohort
Summary
Participatory surveillance systems provide rich crowdsourced data, profiling individuals and their health status at a given time. We explored the usefulness of data from GrippeNet.fr, a participatory surveillance system, to estimate influenza-related illness incidence in France. Different case definitions based on reported signs/symptoms and inclusions of criteria accounting for individuals’ reporting and participation were used to produce influenza-related illness incidence estimates, which were compared to those from sentinel networks. The study provides a standardised analytical framework for crowdsourced surveillance showing high sensitivity in detecting influenza-related changes in the population It contributes to improve the comparability of epidemics across seasons and with sentinel systems. Crowdsourced data bring novel issues regarding data analysis, due to their non-traditional nature They refer to the dynamic participation of individuals having variable reporting behaviours along the season, individuals’ interpretation of the terms used for surveillance, and the correctness of their self-assessments.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
