COVIDmeter - a questionnaire-based symptom monitoring system for the surveillance of COVID-19 in Denmark, 2020-2023

Early in the COVID-19 pandemic, Denmark launched COVIDmeter, a national participatory surveillance platform collecting real-time, self-reported symptoms from a community cohort, aimed to support early signal detection of COVID-like illness. This study describes the community cohort, the reported symptoms among persons testing positive and evaluates COVIDmeter's performance in detecting trends compared to other established surveillance indicators. A total of 143000 individuals registered as participants, of whom 98% completed at least one weekly questionnaire, resulting in approximately 5.8 million responses over the period from March 2020 to March 2023. Of those who tested positive, the most commonly reported symptoms overall were headache, fatigue, muscle or body aches, cough and fever. Trends in COVID-like illness followed similar patterns to other indicators, with COVID-like illness peaks often preceding increases in incidence and hospital admissions, suggesting early detection potential. The study demonstrated that participatory surveillance can serve as an early detection tool for tracking infection trends, particularly in the early stages of a pandemic. While subject to limitations such as selection bias and self-reporting inaccuracies and participatory symptom surveillance proved to be a rapid, scalable and cost-effective complement to traditional surveillance independent of virus testing, this highlights its relevance for future pandemic preparedness.