Leveraging AI-Powered Tools and Big Data for Global Health Surveillance
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).
Deadline for manuscript submissions: 30 May 2026 | Viewed by 50
Special Issue Editors
Interests: development of statistical, mathematical and computational models for climate-sensitive regions; infectious disease modeling; global health surveillance; the use of big data, novel information sources, and tools, including GIS and remote sensing, in public health applications and environmental research
Special Issues, Collections and Topics in MDPI journals
2. Scientific Communications, Sanofi, 450 Water Street, Cambridge, MA 02141, USA
Interests: nutrition surveillance; diet & public health applications; dietary patterns; science communication
Special Issue Information
Dear Colleagues,
This Special Issue highlights advances in leveraging AI-powered tools and big data for global health surveillance, as well as the challenges therein. We aim to provide a broad range of examples, best practices, and emerging and well-tested methodologies in collecting, compiling, analyzing, and communicating large-scale information repositories in various public health fields. We expect contributions to describe, conceptualize, and quantify the pros and cons of new approaches of knowledge generation and synthesis to solve key global health issues like climate change, environmental degradation, population aging, etc. We encourage the submission of interdisciplinary work and multi-country collaborative research in particular, which are crucial for the journal’s broad readership. We invite submissions offering recommendations for improving local, national, and global environmental policies and ethical challenges stemming from new information technologies. We also encourage submissions to address environment- and health-related policies that focus on issues related to global disease dynamics, modern nutrition transition, population movements and changes, and their relationship to the environment and food security. We welcome original research papers using various study designs as well as systematic and narrative reviews and meta-analyses.
Prof. Dr. Elena N. Naumova
Dr. Kristin E. Leonberg
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- global health surveillance
- nutrition surveillance systems
- environmental monitoring
- food security
- disease tracking
- public health and environmental policies
- climate change and extreme weather
- data repositories and dashboards
- artificial intelligence
- data analytics
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