JMIR Publications

ABSTRACT

Background:

The early detection of clusters of infectious diseases, such as the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-related disease (COVID-19), can promote timely testing, recommendation compliance and help 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:

To detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms and examine, a posteriori, the association between clusters’ characteristics and socio-demographic and environmental determinants.

Methods:

This report presents the methodology and development of the @choum (en: “atishoo”) study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 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 application (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 non-sensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (e.g., 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 conducted using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm.

Results:

The study began on September 01st, 2020, and will be completed on February 28th, 2022. Multiple tests conducted at various time points throughout the 5-month preparation phase have helped improve the tool’s user experience and the accuracy of the clustering analyses. A 1-month pilot conducted among 38 pharmacists working in 7 Geneva-based pharmacies has confirmed the proper functioning of the tool. Since the tool’s launch to the entire population of Geneva on February 11th, 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 user’s privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.