The ubiquity of internet-connected technologies has made it easier than ever to gauge the health of an entire community in a scalable manner. Internet browsing and contributing patterns, particularly on social media, provide a powerful lens into people’s wellbeing and their perceptions regarding health issues and events (e.g., epidemics, pandemics). Meanwhile, devices like smartphones and wearables have made it possible to collect physiological, behavioural, and self-reported data without needing to bring people into a clinic. Such digital data can augment public health surveillance, leading to an era of Digital Public Health Surveillance. Compared to traditional electronic health records, these data sources provide a far deeper understanding of the interpersonal and temporal dynamics of public health, enabling new forms of intervention. The insights derived from these data can be used to create surveillance systems that may yield new insights to public health officials and other clinical experts.
The goal of this Research Topic is to explore the opportunities and challenges surrounding digital health beyond personal tracking and instead in public health surveillance. This topic is inherently multi-faceted in that there are questions to be explored at both the individual and population levels. Surveillance typically implies a centralised entity responsible for storing, monitoring, aggregating, and synthesising data, so it is important to ask how these tasks can be accomplished in a scalable, accurate, ethical and equitable manner. The data could be collected from community members through mobile apps, smart devices, internet search engines or social media to make surveillance possible, which opens the door to contributions from technologists, data scientists, psychologists, public health practitioners, designers, and clinicians.
Original research, reviews, protocols, datasets, case studies, and opinion pieces are all welcome. Important subject areas of this Research Topic include but are not limited to:
-Data collection methods that rely on either commodity ubiquitous devices and/or novel hardware that could be distributed throughout a community
-Techniques for data aggregation and synthesis while maintaining individuals’ privacy
-Machine learning techniques that yield insights into the health and wellbeing of populations or subpopulations
-Methods and approaches related to analysis of social media and internet search engine data for digital public health surveillance
-Studies involving digital health surveillance systems that are managed by a centralised entity (e.g., hospitals, public health agencies)
-Investigations into how population-level data should be presented to stakeholders (e.g., human-interpretable summaries and dashboards)
The ubiquity of internet-connected technologies has made it easier than ever to gauge the health of an entire community in a scalable manner. Internet browsing and contributing patterns, particularly on social media, provide a powerful lens into people’s wellbeing and their perceptions regarding health issues and events (e.g., epidemics, pandemics). Meanwhile, devices like smartphones and wearables have made it possible to collect physiological, behavioural, and self-reported data without needing to bring people into a clinic. Such digital data can augment public health surveillance, leading to an era of Digital Public Health Surveillance. Compared to traditional electronic health records, these data sources provide a far deeper understanding of the interpersonal and temporal dynamics of public health, enabling new forms of intervention. The insights derived from these data can be used to create surveillance systems that may yield new insights to public health officials and other clinical experts.
The goal of this Research Topic is to explore the opportunities and challenges surrounding digital health beyond personal tracking and instead in public health surveillance. This topic is inherently multi-faceted in that there are questions to be explored at both the individual and population levels. Surveillance typically implies a centralised entity responsible for storing, monitoring, aggregating, and synthesising data, so it is important to ask how these tasks can be accomplished in a scalable, accurate, ethical and equitable manner. The data could be collected from community members through mobile apps, smart devices, internet search engines or social media to make surveillance possible, which opens the door to contributions from technologists, data scientists, psychologists, public health practitioners, designers, and clinicians.
Original research, reviews, protocols, datasets, case studies, and opinion pieces are all welcome. Important subject areas of this Research Topic include but are not limited to:
-Data collection methods that rely on either commodity ubiquitous devices and/or novel hardware that could be distributed throughout a community
-Techniques for data aggregation and synthesis while maintaining individuals’ privacy
-Machine learning techniques that yield insights into the health and wellbeing of populations or subpopulations
-Methods and approaches related to analysis of social media and internet search engine data for digital public health surveillance
-Studies involving digital health surveillance systems that are managed by a centralised entity (e.g., hospitals, public health agencies)
-Investigations into how population-level data should be presented to stakeholders (e.g., human-interpretable summaries and dashboards)