Chiolero, ArnaudArnaudChioleroTancredi, StefanoStefanoTancrediIoannidis, John P AJohn P AIoannidis2024-10-252024-10-252023-12https://boris-portal.unibe.ch/handle/20.500.12422/170436Surveillance and research data, despite their massive production, often fail to inform evidence-based and rigorous data-driven health decision-making. In the age of infodemic, as revealed by the COVID-19 pandemic, providing useful information for decision-making requires more than getting more data. Data of dubious quality and reliability waste resources and create data-genic public health damages. We call therefore for a slow data public health, which means focusing, first, on the identification of specific information needs and, second, on the dissemination of information in a way that informs decision-making, rather than devoting massive resources to data collection and analysis. A slow data public health prioritizes better data, ideally population-based, over more data and aims to be timely rather than deceptively fast. Applied by independent institutions with expertise in epidemiology and surveillance methods, it allows a thoughtful and timely public health response, based on high-quality data fostering trustworthiness.enBig data Evidence-based public health Infodemic Surveillance600 - Technology::610 - Medicine & health300 - Social sciences, sociology & anthropology::360 - Social problems & social servicesarticle10.48350/1868923778922510.1007/s10654-023-01049-6