Leveraging deeplearning and unconventional data for realtime surveillance forecasting and early warning of respiratory pathogens outbreak
| dc.article.end-page | 18 | en |
| dc.article.start-page | 1 | en |
| dc.citation.doi | 10.1016/J.ARTMED.2025.103076 | en |
| dc.contributor.author | Z. Movahedi Nia | en |
| dc.contributor.author | L. Seyyed-Kalantari | en |
| dc.contributor.author | M. Goitom | en |
| dc.contributor.author | Bruce Mellado | en |
| dc.contributor.author | A. Ahmadi | en |
| dc.contributor.author | al et | en |
| dc.date.accessioned | 2025-08-22T08:40:50Z | |
| dc.faculty | FACULTY OF SCIENCE | en |
| dc.identifier.citation | WOS | en |
| dc.identifier.issn | 0933-3657 | en |
| dc.identifier.uri | https://hdl.handle.net/10539/46008 | |
| dc.journal.title | Leveraging deeplearning and unconventional data for realtime surveillance forecasting and early warning of respiratory pathogens outbreak | en |
| dc.journal.volume | 161 | en |
| dc.publisher | ELSEVIER SCIENCE BV | en |
| dc.title | Leveraging deeplearning and unconventional data for realtime surveillance forecasting and early warning of respiratory pathogens outbreak | en |
| dc.type | Journal Article | en |
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