Research Topic

When Data Science, Humanities and Social Sciences Meet: Cross-Talks and Insights in Public Health

About this Research Topic

Big data is still an emerging, fast-changing topic in medicine, but it originated in the older and larger field of data science. Data science is about collecting and merging data of various natures from diverse sources, but also has to do with mining data for information retrieval and providing observation-based support for decision-making. If we refer to broad definitions of health such as WHO’s definition, Engels’ biopsychosocial definition or even the syndemics approach, data science may appear as an unexpected opportunity to consider health in a very comprehensive, multidimensional way as well as from a real-life point of view, but based on data. It could be seen as an extension of the evidence-based approach of health. Public health addresses an even broader range of challenges by taking into account from the (sub)individual to the societal and global scales to build its knowledge and ground its actions. As of today, by heavily relying on biomedical, quantitative techniques, the field of public health faces all kind of issues related to real life, individual and social complexity.

Historically, the social sciences and humanities (SSH) had to face this complexity and had to prove that they were autonomous scientific fields, as Durkheim managed to do by grounding sociology as a science by defining the field’s own scientific method. SSH researchers developed and tried many methodological approaches. As a result, SSH may be methodologically richer than the biomedical field, which is mainly restricted to statistical methods. The main source of data is currently some unstructured data, e.g. text or voice. What is at stake is to take the best of the two worlds, from quantitative methods and from SSH approaches.

Data science applied to health and public health enables a genuine data-driven, evidence-based policy making. We therefore change our former world of rigid guidelines for a more prescription-oriented world. SSH are all the most needed to question the ethics and laws of applied data science: when action is possible, should it be actually performed? Data science questions how and how far behavioral changes should be done, how mass monitoring and control are to be authorized. SSH specialists are used to these questions and can provide critical clues. SSH researchers have their own ways of questioning our world: these ways of questioning should be translated and infuse data science with new approaches and insights. Conversely, data science can enrich SSH toolbox with hybrid or novel quantitative methods. Finally, a “meet me halfway” strategy should be considered so that tools designed for both SSH researchers and data scientists are conceived. A series of cross-talks between data scientists and SSH researchers could provide valuable insights for public health.

The following, non-exhaustive themes will be considered:

- Examples and insights from other fields using data science that could be used for guidance in health
- How data science may help SSH researchers
- How SSH researchers may help data scientists
- Health policies and health insurances
- Evidence-based policies, complex interventions
- Multi-objectives optimization and individual-society interactions


Keywords: big data, sociology, machine learning, public health, data science


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Big data is still an emerging, fast-changing topic in medicine, but it originated in the older and larger field of data science. Data science is about collecting and merging data of various natures from diverse sources, but also has to do with mining data for information retrieval and providing observation-based support for decision-making. If we refer to broad definitions of health such as WHO’s definition, Engels’ biopsychosocial definition or even the syndemics approach, data science may appear as an unexpected opportunity to consider health in a very comprehensive, multidimensional way as well as from a real-life point of view, but based on data. It could be seen as an extension of the evidence-based approach of health. Public health addresses an even broader range of challenges by taking into account from the (sub)individual to the societal and global scales to build its knowledge and ground its actions. As of today, by heavily relying on biomedical, quantitative techniques, the field of public health faces all kind of issues related to real life, individual and social complexity.

Historically, the social sciences and humanities (SSH) had to face this complexity and had to prove that they were autonomous scientific fields, as Durkheim managed to do by grounding sociology as a science by defining the field’s own scientific method. SSH researchers developed and tried many methodological approaches. As a result, SSH may be methodologically richer than the biomedical field, which is mainly restricted to statistical methods. The main source of data is currently some unstructured data, e.g. text or voice. What is at stake is to take the best of the two worlds, from quantitative methods and from SSH approaches.

Data science applied to health and public health enables a genuine data-driven, evidence-based policy making. We therefore change our former world of rigid guidelines for a more prescription-oriented world. SSH are all the most needed to question the ethics and laws of applied data science: when action is possible, should it be actually performed? Data science questions how and how far behavioral changes should be done, how mass monitoring and control are to be authorized. SSH specialists are used to these questions and can provide critical clues. SSH researchers have their own ways of questioning our world: these ways of questioning should be translated and infuse data science with new approaches and insights. Conversely, data science can enrich SSH toolbox with hybrid or novel quantitative methods. Finally, a “meet me halfway” strategy should be considered so that tools designed for both SSH researchers and data scientists are conceived. A series of cross-talks between data scientists and SSH researchers could provide valuable insights for public health.

The following, non-exhaustive themes will be considered:

- Examples and insights from other fields using data science that could be used for guidance in health
- How data science may help SSH researchers
- How SSH researchers may help data scientists
- Health policies and health insurances
- Evidence-based policies, complex interventions
- Multi-objectives optimization and individual-society interactions


Keywords: big data, sociology, machine learning, public health, data science


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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01 March 2018 Manuscript

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Manuscripts can be submitted to this Research Topic via the following journals:

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Submission Deadlines

01 March 2018 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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