Platforms for artificial intelligence-enabled infectious disease surveillance
Abstract Artificial intelligence (AI)-enabled infectious disease surveillance platforms are expanding rapidly, but their roles within health systems remain poorly characterized. This review examined 20 platforms according to primary data inputs, surveillance function, AI methods, data privacy, geographic scope and pathogen focus. Three archetypes emerged: Early Warning Networks, Situational Awareness Platforms and Integrated Surveillance Platforms. Their comparative analysis highlighted how data type, integration and localization shape platform contributions to public-health decision making.
Authors
- Marya Getchell (ORCID: https://orcid.org/0000-0002-5831-4008)
- Carl J. E. Suster (ORCID: https://orcid.org/0000-0001-7021-9380)
- Ahmad Watsiq Maula (ORCID: https://orcid.org/0000-0003-1494-5403)
- Yoong Khean Khoo (ORCID: https://orcid.org/0000-0001-7099-5566)
- Timothy J. Dallman
- David B. Hipgrave
- Philip AbdelMalik
- Suci Wulandari
- Michael Barber
- Shurendar Kumar
- Vitali Sintchenko
- Thomas Carpino
- Junxiong Pang
- Paul M. Pronyk
- Ashley Tsai
- Nan Liu
Institutions
- World Health Organization - Pakistan (PK)
- The University of Sydney (AU)
- New South Wales Department of Health (AU)
- Universitas Gadjah Mada (ID)
- National University of Singapore (SG)
- Duke University (US)
- Taronga Conservation Society Australia (AU)
- SingHealth Duke-NUS Academic Medical Centre (SG)
- World Health Organization (CH)
- Duke Institute for Health Innovation (US)
- Duke-NUS Medical School (SG)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-09-04
- DOI
- https://doi.org/10.1038/s41746-026-03189-x
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Bill and Melinda Gates Foundation
- National University of Singapore
- Temasek Foundation