Epidemiological methods in transition: Minimizing biases in classical and digital approaches.
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000670 |
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| author | Sara Mesquita Lília Perfeito Daniela Paolotti Joana Gonçalves-Sá |
| author_facet | Sara Mesquita Lília Perfeito Daniela Paolotti Joana Gonçalves-Sá |
| author_sort | Sara Mesquita |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f869175cae104fe9b9127b712c59122f |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-f869175cae104fe9b9127b712c59122f2025-02-05T05:33:36ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-01-0141e000067010.1371/journal.pdig.0000670Epidemiological methods in transition: Minimizing biases in classical and digital approaches.Sara MesquitaLília PerfeitoDaniela PaolottiJoana Gonçalves-SáEpidemiology 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.https://doi.org/10.1371/journal.pdig.0000670 |
| spellingShingle | Sara Mesquita Lília Perfeito Daniela Paolotti Joana Gonçalves-Sá Epidemiological methods in transition: Minimizing biases in classical and digital approaches. PLOS Digital Health |
| title | Epidemiological methods in transition: Minimizing biases in classical and digital approaches. |
| title_full | Epidemiological methods in transition: Minimizing biases in classical and digital approaches. |
| title_fullStr | Epidemiological methods in transition: Minimizing biases in classical and digital approaches. |
| title_full_unstemmed | Epidemiological methods in transition: Minimizing biases in classical and digital approaches. |
| title_short | Epidemiological methods in transition: Minimizing biases in classical and digital approaches. |
| title_sort | epidemiological methods in transition minimizing biases in classical and digital approaches |
| url | https://doi.org/10.1371/journal.pdig.0000670 |
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