Towards real-time monitoring of social contacts via participatory disease surveillance
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Social contacts are key drivers of infectious disease transmission, yet most available data on contact behavior come from stand-alone surveys that are resource-intensive, infrequent, and limited in scope. Digital participatory surveillance offers a promising alternative, enabling continuous collection of health-related data with fewer resources. In this study, we analyzed the first year of contact data collected through Influweb, the Italian branch of the European InfluenzaNet network, starting in February 2024. We linked 1, 393 contact diaries with self-reported symptoms and participant characteristics, applying a zero-inflated negative binomial regression model to assess demographic, temporal, and health-related correlates of contact behavior. Based on weighted sample estimates, symptomatic participants reported higher median contacts. In the regression model, mild symptoms were associated with a borderline-significant 20% increase in contacts compared to asymptomatic individuals. Contacts were also higher on weekdays and for employed participants, lower during holidays, and varied by age. In parallel, we constructed age-stratified contact matrices describing contact rates across settings and months. Comparisons with prior studies showed concordance in structural features, including age-assortative mixing in schools and workplaces and intergenerational mixing at home, while overall contact volumes were lower, consistent with recent post-pandemic evidence. These findings demonstrate the potential of digital participatory surveillance for real-time monitoring of social contacts, highlighting its added value in linking contact behavior with health and sociodemographic information, and its potential as a scalable complement to traditional contact surveys.
Author summary
Infectious diseases spread through the social contacts we make in daily life, but collecting detailed information on these contacts is usually done through specialized surveys that are costly, infrequent, and limited in scope. In this study, we tested whether digital participatory surveillance—where volunteers regularly share health information online—can also be used to monitor contact behavior. We invited participants in Influweb, the Italian participatory platform, to report their social contacts and analyzed the first year of responses. By linking contact reports with participants’ symptoms and characteristics, we found that people with mild symptoms reported more contacts than those without symptoms. Contact numbers were also higher on weekdays, lower during holidays, and varied by age and employment status. We also constructed contact patterns by age and setting, which reproduced well-known features of social mixing, while overall volumes were lower than in pre-pandemic surveys and consistent with more recent studies. Our findings show that participatory platforms can provide timely and flexible insights into how social behavior and health interact, offering a scalable complement to traditional surveys for understanding disease transmission.
