Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study

Article Article scientifique Article Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study eng 2021-10-06 2021-10-06 author De Ridder David 0000-0001-6462-2501 886986 author Loizeau Andréa 0000-0001-6964-2501 677311 author Sandoval José Luis 0000-0002-6256-7083 author Ehrler Frédéric 0000-0001-9734-3242 509959 author Perrier Myriam 0000-0001-9005-4245 author Ritch Albert 0000-0003-4296-1400 500183 author Violot Guillemette 0000-0001-9017-1193 author Santolini Marc 0000-0003-1491-0120 author Greshake Tzovaras Bastian 0000-0002-9925-9623 author Stringhini Silvia 0000-0002-4387-8943 830749 author Kaiser Laurent 0000-0002-0857-2252 28229 author Pradeau Jean-François 0000-0002-0998-817X 904364 author Joost Stéphane 0000-0002-1184-7501 133845 author Guessous Idris 0000-0002-0491-6089 760565 e30444 10.2196/30444 34449403 1929-0748 PMC8496683 https://www.researchprotocols.org/2021/10/e30444 Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic. Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants. Methods: This report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm. Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022. Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing. International registered report identifier (irrid): DERR1-10.2196/30444. COVID-19 SARS-CoV-2 Digital health Digital surveillance Epidemiology Infectious disease MHealth Mobile app Participatory surveillance Public health Space-time clustering Surveillance Dewey 613 Dewey 616 56 Département de médecine 221 64 Département de santé et médecine communautaires 22A false 400 Groupe Laurent Kaiser (virologie clinique) 668 1313 Médecine de 1er recours 1020 JMIR research protocols 10 10 a795ac9d-364d-4553-a994-432721dadca7 Article (Published version) Public JMIR_DDeRidder_Detection of Spatiot.pdf 858608 application/pdf CC BY-4.0 990253ad-41ae-4609-8dfd-c0331b63f5c4 Appendix Public resprot_v10i10e30444_app1.pdf 5867406 application/pdf openAccess Faculté de médecine / Section de médecine clinique / Département de médecine Faculté de médecine / Section de médecine clinique / Département de santé et médecine communautaires

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Detection of Spatiotemporal Clusters of COVID-19–Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study	JMIR research protocols			2021	128	56

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