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

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 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.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

“…Kandula et al ( 93 ) developed a compartmental model for predicting influenza hospitalization rates using Google search trends. Moa et al ( 94 ) proposed a linear model to forecast the overall severity of an influenza season in Australia based on only five parameters.…”

Preventing the next pandemic: Use of artificial intelligence for epidemic monitoring and alerts

“…The internal application contains full data access, dashboards with relevant visualizations (for mapping, statistical analytics, searching, or decision support), a user administration panel, and searchable access to the database. EPIWATCH is supplemented by the development of tools such as a seasonal influenza forecasting tool (FLUCAST), 7 an epidemic risk analysis tool (EPIRISK), 8 and a tool for determining the origins of epidemics, 9 all of which have been tested through rigorous research.…”

Comparative epidemiology, phylogenetics, and transmission patterns of severe influenza A/H3N2 in Australia from 2003 to 2017

“…1 The predicted severity of seasonal influenza is not well understood but often depends on the predominate subtype in the circulation, vaccination coverage in the population, and the individual immune response of infected persons. 2 Studies have frequently observed higher rates of hospitalization and reduced vaccine effectiveness among A/H3N2 predominate seasons when compared to seasonal A/H1N1pdm09 (and the pre-pandemic seasonal A/H1N1) and influenza B viruses. [3][4][5] Increased disease burden and mortality in elderly people and children have also been associated with A/H3N2 infection compared with other seasonal strains, 6,7 while influenza B typically causes disproportionate morbidity and mortality in children.…”

“…Influenza A/H3N2 is one of four subtypes that circulate seasonally in humans every year causing annual epidemics in both temperate and tropical regions; the others being influenza A H1N1pdm09 (A/H1N1pdm09) and influenza B Yamagata (B/Yam) and Victoria (B/Vic) 1 . The predicted severity of seasonal influenza is not well understood but often depends on the predominate subtype in the circulation, vaccination coverage in the population, and the individual immune response of infected persons 2 . Studies have frequently observed higher rates of hospitalization and reduced vaccine effectiveness among A/H3N2 predominate seasons when compared to seasonal A/H1N1pdm09 (and the pre‐pandemic seasonal A/H1N1) and influenza B viruses 3‐5 .…”