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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Aug 2, 2018
Date Accepted: Jun 18, 2019

The final, peer-reviewed published version of this preprint can be found here:

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

Moa A, Muscatello D, Chughtai A, Chen X, MacIntyre CR

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

JMIR Public Health Surveill 2019;5(3):e11780

DOI: 10.2196/11780

PMID: 31339102

PMCID: 6683655

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

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

  • Aye Moa; 
  • David Muscatello; 
  • Abrar Chughtai; 
  • Xin Chen; 
  • C Raina MacIntyre

Background:

Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza.

Objective:

This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season.

Methods:

Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season.

Results:

The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning.

Conclusions:

The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.


 Citation

Please cite as:

Moa A, Muscatello D, Chughtai A, Chen X, MacIntyre CR

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

JMIR Public Health Surveill 2019;5(3):e11780

DOI: 10.2196/11780

PMID: 31339102

PMCID: 6683655

Per the author's request the PDF is not available.