JMIR Publications

ABSTRACT

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 indicated a preference of 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, Department of Health, Australia. We tested Flucast using retrospective seasonal data for eleven Australian influenza seasons. We compared five different models, using parameters known early in the season and which 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. A model with 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 emerging 2018 influenza season.

Results:

The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. For 2018, the tool provided a reliable early prediction of severe seasonal influenza with the use of real-time data. 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. Clinical Trial: NA