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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.

Circulating influenza was associated with sustained increases and peaks in delayed patient throughput and premature patient departures. Influenza surveillance information may assist with development of health system and hospital workforce planning and bed management activities.

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Objective: Programmes that reduce the time to defibrillation are likely to improve overall survival rates from out-of-hospital cardiac arrests (OHCAs). This research sought to identify human factors common among community responders taking an automated external defibrillator (AED) to a victim of an OHCA that are either barriers or enablers of desired behaviour. Methods: A qualitative methodology was used. Community members who had access to an AED and who had been notified of an incident of OHCA near them were approached to participate in the research. Participants completed a written survey and undertook a semi-structured interview. A thematic analysis was undertaken using NVivo software and triangulated against findings from an automated data-mining package, Leximancer. Results: The study found that 100% of people who were notified of the need for an AED responded. Twelve participants subsequently identified during interviews that they held some form of leadership role in their community. First aid training and previous experience of, and competency in managing emergencies were the strongest motivations for their response. Personal risk was not a concern when responding to victims in immediately life-threatening situations.Conclusion: Prospective programmes may be able to be designed to increase the likelihood that community members with AEDs will respond in advance of emergency medical services by targeting common human factors, such as leadership behaviour, training, competency and experience in managing emergencies, leading to better overall survival rates from OHCA.