Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak
This work presents a framework that automatically collects data from unconventional sources, and builds an early warning system for COVID-19 and influenza outbreaks. The result is extremely helpful to policy-makers and health officials for preparedness and rapid response against future outbreaks.
Authors
- Laleh Seyyed-Kalantari (ORCID: https://orcid.org/0000-0002-1059-7125)
- Mary Goitom (ORCID: https://orcid.org/0000-0002-1175-8904)
- Ali Asgary (ORCID: https://orcid.org/0000-0002-6253-1021)
- Ali Ahmadi (ORCID: https://orcid.org/0000-0003-4211-6258)
- Jude Dzevela Kong (ORCID: https://orcid.org/0000-0002-7557-5672)
- James Orbinski (ORCID: https://orcid.org/0000-0002-0186-1406)
- Zahra Movahedi Nia (ORCID: https://orcid.org/0000-0002-5528-638X)
- Jian Wu (ORCID: https://orcid.org/0000-0002-3394-1507)
- B. Mellado
Institutions
- York University (CA)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Artificial Intelligence in Medicine
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1016/j.artmed.2025.103076
- Citations
- 6
- Primary Topic
- Data-Driven Disease Surveillance
- Type
- article
- Field-Weighted Citation Impact
- 8.13
Funders
- International Development Research Centre
- Foreign, Commonwealth and Development Office
- Natural Sciences and Engineering Research Council of Canada
- Social Sciences and Humanities Research Council of Canada