  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.
