Research Topic

Extracting Insights from Digital Public Health Data using Artificial Intelligence

About this Research Topic

Artificial Intelligence (AI) has the ability to perform automated/case-based reasoning, constraint processing, deep learning, and deep reinforcement learning. Recent advancements in AI techniques and GPU (graphics processing unit) computing capabilities have made it possible to process large volumes of data and extract valuable insights within a short period. Digital public health data are enormous, and harnessing AI's power can lead to exciting and ground-breaking research. Due to the current COVID-19 pandemic, AI can assist in disease surveillance methods, infectious disease modeling, non-contact temperature screening, intelligent contact tracking, detecting social/economic factors on transmission, effective health communication and misinformation detection, identifying factors that affect the mental and emotional health of the public.

The objective of this Research Topic would be to provide a platform for researchers, health practitioners, policymakers, and governments to apply the current state-of-the-art AI techniques on digital public health data and obtain critical insights. The Topic Editors welcome original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences in the area of application of AI on digital public health data.

Manuscripts of interest include, but not restricted to:
• Predictive analytics on digital public health data using deep learning;
• Early warning and rapid response AI tools for disease outbreaks and epidemics;
• Preserving privacy and confidentially data using adversarial machine learning;
• Infectious disease modeling using generative adversarial networks;
• Surveillance of infectious disease and epidemic intelligence;
•Non-contact temperature screening using thermal imaging and computer vision;
• Health communication and misinformation detection using recurrent neural networks;
• Analyzing mental and emotional health using meta-learning;
• AI techniques using novel data sources to improve public health outcomes.


Keywords: Digital Public Health, Disease Surveillance, Artificial Intelligence, Deep Learning, Deep Reinforcement Learning


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.

Artificial Intelligence (AI) has the ability to perform automated/case-based reasoning, constraint processing, deep learning, and deep reinforcement learning. Recent advancements in AI techniques and GPU (graphics processing unit) computing capabilities have made it possible to process large volumes of data and extract valuable insights within a short period. Digital public health data are enormous, and harnessing AI's power can lead to exciting and ground-breaking research. Due to the current COVID-19 pandemic, AI can assist in disease surveillance methods, infectious disease modeling, non-contact temperature screening, intelligent contact tracking, detecting social/economic factors on transmission, effective health communication and misinformation detection, identifying factors that affect the mental and emotional health of the public.

The objective of this Research Topic would be to provide a platform for researchers, health practitioners, policymakers, and governments to apply the current state-of-the-art AI techniques on digital public health data and obtain critical insights. The Topic Editors welcome original, high-quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences in the area of application of AI on digital public health data.

Manuscripts of interest include, but not restricted to:
• Predictive analytics on digital public health data using deep learning;
• Early warning and rapid response AI tools for disease outbreaks and epidemics;
• Preserving privacy and confidentially data using adversarial machine learning;
• Infectious disease modeling using generative adversarial networks;
• Surveillance of infectious disease and epidemic intelligence;
•Non-contact temperature screening using thermal imaging and computer vision;
• Health communication and misinformation detection using recurrent neural networks;
• Analyzing mental and emotional health using meta-learning;
• AI techniques using novel data sources to improve public health outcomes.


Keywords: Digital Public Health, Disease Surveillance, Artificial Intelligence, Deep Learning, Deep Reinforcement Learning


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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Submission Deadlines

31 May 2021 Abstract
30 June 2021 Manuscript

Participating Journals

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

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Topic Editors

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Submission Deadlines

31 May 2021 Abstract
30 June 2021 Manuscript

Participating Journals

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

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