From GeoSentinel data to epidemiological insights: a multidisciplinary effort towards artificial intelligence-supported detection of infectious disease outbreaks

J Travel Med. 2024 Jan 17:taae013. doi: 10.1093/jtm/taae013. Online ahead of print.

Stan Heidema¹, Ivo V Stoepker¹, Gerard Flaherty^{2

3}, Kristina M Angelo⁴, Richard A J Post¹, Charles Miller⁴, Michael Libman⁵, Davidson H Hamer^{6

7

8}, Edwin R van den Heuvel^{1

9}, Ralph Huits¹⁰

¹ Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
² School of Medicine, University of Galway, Galway, Ireland.
³ School of Medicine, International Medical University, Kuala Lumpur, Malaysia.
⁴ Division of Global Migration and Health, National Center for Emerging and Zoonotic Infectious Disease, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
⁵ J.D. MacLean Centre for Tropical Diseases, McGill University, Montreal, Canada.
⁶ Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA.
⁷ Section of Infectious Diseases, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts, USA.
⁸ Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, Massachusetts, USA.
⁹ Department of Preventive Medicine and Epidemiology, School of Medicine, Boston University, USA.
¹⁰ Department of Infectious Tropical Diseases and Microbiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Verona, Italy.

No abstract available

Keywords: Artificial intelligence; Data science; Disease surveillance; Outbreak detection; Sentinel event; Travel medicine.

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