While the exponential proliferation of data has been described for decades, the “big data” phenomenon has more recently become a cross-disciplinary area of significant attention and development for public health. We are increasingly able to process, mine and analyse more and more data faster than ever before, enabled by rapid advances in technology and connected citizen engagement around the globe. The continued growth of society's contribution to data and technology presents incredible opportunities for public health. Many research activities have been exploring the use of big data and associated technologies within the public health landscape, ranging from event detection and early warning to targeting health behavior and assessing interventions. At the same time, however, the use of big data has also come with growing and legitimate concerns, such as privacy, data quality, interpretability and analytical validity.
This research topic of Frontiers in Public Health: Digital Health section focuses on cutting-edge approaches to big data use in public health as well as their critical assessment. The data streams discussed might include linked data from traditional surveillance systems, mobile GPS data, personalized data from health intervention apps, participatory surveillance, sensor data, social media, online search and more. Research investigating cross-linking of various data streams is particularly welcomed, as well as applications of machine learning, natural language processing, and signal processing.
While much has been published in this area, methods and applications continue to evolve, fueled by an increasing cross-pollination between disciplines, changing competencies and limitless creativity. We encourage submissions from researchers and practitioners across academia, industry and government, highlighting theoretical and applied uses of big data to advance public health practice and improve the health of populations around the globe. Papers may also highlight positive and/or negative implications of big data use. Submissions will be assessed based on novelty, creativity and contribution across domains.
While the exponential proliferation of data has been described for decades, the “big data” phenomenon has more recently become a cross-disciplinary area of significant attention and development for public health. We are increasingly able to process, mine and analyse more and more data faster than ever before, enabled by rapid advances in technology and connected citizen engagement around the globe. The continued growth of society's contribution to data and technology presents incredible opportunities for public health. Many research activities have been exploring the use of big data and associated technologies within the public health landscape, ranging from event detection and early warning to targeting health behavior and assessing interventions. At the same time, however, the use of big data has also come with growing and legitimate concerns, such as privacy, data quality, interpretability and analytical validity.
This research topic of Frontiers in Public Health: Digital Health section focuses on cutting-edge approaches to big data use in public health as well as their critical assessment. The data streams discussed might include linked data from traditional surveillance systems, mobile GPS data, personalized data from health intervention apps, participatory surveillance, sensor data, social media, online search and more. Research investigating cross-linking of various data streams is particularly welcomed, as well as applications of machine learning, natural language processing, and signal processing.
While much has been published in this area, methods and applications continue to evolve, fueled by an increasing cross-pollination between disciplines, changing competencies and limitless creativity. We encourage submissions from researchers and practitioners across academia, industry and government, highlighting theoretical and applied uses of big data to advance public health practice and improve the health of populations around the globe. Papers may also highlight positive and/or negative implications of big data use. Submissions will be assessed based on novelty, creativity and contribution across domains.
