TY - JOUR
T1 - Epidemiological methods in transition
T2 - minimizing biases in classical and digital approaches
AU - Mesquita, Sara
AU - Perfeito, Lília
AU - Paolotti, Daniela
AU - Gonçalves-Sá, Joana
N1 - Copyright: © 2025 Mesquita et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This paper was partially supported by FCT grant DSAIPA/AI/0087/2018 to JGS, 2024.07331.IACDC to LP, and by Ph. D. fellowship 2020.10157.BD to SM. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
PY - 2025/1
Y1 - 2025/1
N2 - 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 diseases, we review the state of Digital Epidemiology at the beginning of 2020 and how it changed after the COVID-19 pandemic, in both nature and breadth. We argue that Epidemiology's progressive use of data generated outside of clinical and public health systems creates several technical challenges, particularly in carrying specific biases that are almost impossible to correct for a priori. Using a statistical perspective, we discuss how a definition of Digital Epidemiology that emphasizes "data-type" instead of "data-source," may be more operationally useful, by clarifying key methodological differences and gaps. Therefore, we briefly describe some of the possible biases arising from varied collection methods and sources, and offer some recommendations to better explore the potential of Digital Epidemiology, particularly on how to help reduce inequity.
AB - 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 diseases, we review the state of Digital Epidemiology at the beginning of 2020 and how it changed after the COVID-19 pandemic, in both nature and breadth. We argue that Epidemiology's progressive use of data generated outside of clinical and public health systems creates several technical challenges, particularly in carrying specific biases that are almost impossible to correct for a priori. Using a statistical perspective, we discuss how a definition of Digital Epidemiology that emphasizes "data-type" instead of "data-source," may be more operationally useful, by clarifying key methodological differences and gaps. Therefore, we briefly describe some of the possible biases arising from varied collection methods and sources, and offer some recommendations to better explore the potential of Digital Epidemiology, particularly on how to help reduce inequity.
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85215297266&origin=resultslist&sort=plf-f&src=s&sid=1690e2c78bdf7cb98bd7bb30dc7c7292&sot=a&sdt=a&s=SOURCE-ID+%2821101242952%29&sl=23&sessionSearchId=1690e2c78bdf7cb98bd7bb30dc7c7292&relpos=3
U2 - 10.1371/journal.pdig.0000670
DO - 10.1371/journal.pdig.0000670
M3 - Review article
C2 - 39804936
SN - 2767-3170
VL - 4
JO - PLOS Digital Health
JF - PLOS Digital Health
IS - 1
M1 - e0000670
ER -