2023/12/08 by Sara Mesquita, Mesquita, Sara, Lília Perfeito +5
Computer Science · Mathematics · Medicine · #Applications (stat.AP) #COVID-19 Digital Contact Tracing #COVID-19 epidemiological studies #Computers and Society (cs.CY) #Data-Driven Disease Surveillance #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2312.04835
openalex publication_date 2023/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.