Epidemiology and Public Health have increasingly relied on structured and unstructured data, collected inside and outside of typical health systems, to study, identify, and mitigate diseases at the population level. Focusing on infectious disease, we review how Digital Epidemiology (DE) was at the beginning of 2020 and how it was changed by the COVID-19 pandemic, in both nature and breadth. We argue that DE will become a progressively useful tool as long as its potential is recognized and its risks are minimized. Therefore, we expand on the current views and present a new definition of DE that, by highlighting the statistical nature of the datasets, helps in identifying possible biases. We offer some recommendations to reduce inequity and threats to privacy and argue in favour of complex multidisciplinary approaches to tackling infectious diseases.

翻译：流行病学与公共卫生日益依赖在典型卫生系统内外收集的结构化和非结构化数据，以在人群层面研究、识别和缓解疾病。聚焦传染病领域，我们回顾了2020年初数字流行病学（DE）的状态，以及COVID-19大流行如何从根本上改变了其性质与广度。我们认为，只要DE的潜力得到认可且风险被最小化，它将逐步成为更有用的工具。因此，我们扩展了现有观点，提出DE的新定义——通过强调数据集的统计特性，该定义有助于识别潜在偏差。我们提出一些建议以减少不平等和隐私威胁，并主张采用复杂的多学科方法应对传染病。