About this Research Topic
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.