This paper is in the following e-collection/theme issue:

Early Results in Infodemiology and Infoveillance (104) Infodemiology and Infoveillance (1558) Outbreak and Pandemic Preparedness and Management (1826) Public (e)Health, Digital Epidemiology and Public Health Informatics (1008)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46087, first published .
A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

Authors of this article:

Mahakprit Kaur1, 2 Author Orcid Image ;   Taylor Cargill1, 2 Author Orcid Image ;   Kevin Hui2, 3 Author Orcid Image ;   Minh Vu2, 3 Author Orcid Image ;   Nicola Luigi Bragazzi4, 5 Author Orcid Image ;   Jude Dzevela Kong2, 4 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Feriz A, Jalali M, Forghani Y. Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling. Concurrency and Computation: Practice and Experience 2024;36(17) View
  2. Shafiya S, Wani M, Jabin S, ELAffendi M, Jahiruddin . CoViNAR: a context-aware social media dataset for pandemic severity level prediction and analysis. Frontiers in Artificial Intelligence 2025;8 View
  3. Bakhshaei K, Rezaei Z, Ahmadi M, Banad Y. Pandemic transition: A review of social media text mining for pandemic transition in the post-vaccination era. Artificial Intelligence in Medicine 2025;169:103242 View