Time series analysis has been applied to the management of Emergency Departments (EDs) to describe and predict presentation patterns. Capacity to manage these predictable surges can be planned in advance. In the midst of the CoVID-19 pandemic, it is especially important for health services to make data-driven decisions regarding management of respiratory infections. This study aims to engage time series analysis techniques to inform data-driven decision-making for this and future influenza seasons.
All patients who present with the diagnostic codes J069 (Acute upper respiratory infection, unspecified), J111 (influenza with other respiratory manifestations, virus not identified), J22 (unspecified acute lower respiratory infection), and R05 (cough) presenting to an Australian metropolitan Emergency Department will be counted between July 2014 and February 2020. Time series analysis will be used to describe seasonality, trend, and error. Subgroup analysis will be performed for category 1 and 2 patients. Forecasting of the next two influenza seasons will be attempted, and reported with 95% confidence intervals. Data analysis will be performed in R Studio v1.2.5033.
This study is not funded and will be performed in the Chief Investigator's spare time.