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

Surveillance Systems (466) Infodemiology and Infoveillance (1369) Infectious Diseases (non-STD/STI) (1592) Influenza and Influenza-Like-Illness (ILI) Surveillance (219)

Published on in Vol 5, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11780, first published .
Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Authors of this article:

Aye Moa1 Author Orcid Image ;   David Muscatello2 Author Orcid Image ;   Abrar Chughtai2 Author Orcid Image ;   Xin Chen1 Author Orcid Image ;   C Raina MacIntyre1, 3 Author Orcid Image
Article Authors Cited by (5) Tweetations (5) Metrics

Journals

  1. Xia J, Adam D, Moa A, Chughtai A, Barr I, Komadina N, MacIntyre C. Comparative epidemiology, phylogenetics, and transmission patterns of severe influenza A/H3N2 in Australia from 2003 to 2017. Influenza and Other Respiratory Viruses 2020;14(6):700 View
  2. Botz J, Wang D, Lambert N, Wagner N, Génin M, Thommes E, Madan S, Coudeville L, Fröhlich H. Modeling approaches for early warning and monitoring of pandemic situations as well as decision support. Frontiers in Public Health 2022;10 View
  3. MacIntyre C, Lim S, Quigley A. Preventing the next pandemic: Use of artificial intelligence for epidemic monitoring and alerts. Cell Reports Medicine 2022;3(12):100867 View
  4. MacIntyre C, Chen X, Kunasekaran M, Quigley A, Lim S, Stone H, Paik H, Yao L, Heslop D, Wei W, Sarmiento I, Gurdasani D. Artificial intelligence in public health: the potential of epidemic early warning systems. Journal of International Medical Research 2023;51(3) View
  5. Morbey R, Todkill D, Watson C, Elliot A, Freitas A. Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season. PLOS ONE 2023;18(9):e0291932 View