Temporal analysis of respiratory virus epidemics in Victoria over winter 2024

During winter months of temperate regions, concurrent epidemics of multiple respiratory pathogens can occur, causing periods of increased clinical burden. Case time series, which are predominantly used to monitor infection levels, can exhibit substantial noise and day-of-the-week effects, limiting the visual interpretation of trends in raw data. However, statistical methods can infer smoothed trends within case time series by quantifying and accounting for different sources of noise. Here we apply statistical models to estimate the epidemic dynamics of SARS-CoV-2, respiratory syncytial virus (RSV), and influenza subtypes (influenza A H3N2, influenza A H1N1, and influenza B) in Victoria, Australia, over the 2024 winter season. We model trends in daily reported cases and the daily growth rate over time for all pathogens/subtypes. We present: (1) retrospective analyses using the final dataset up to 10 September 2024 and (2) weekly real-time analyses from 19 March 2024 to 10 September 2024 using data up to each timepoint, including a retrospective performance evaluation. We estimated similar peak timing of SARS-CoV-2 and RSV epidemics in late May, followed by a H3N2-dominant influenza epidemic, which peaked in early July. Transient increases in SARS-CoV-2 activity coincided with the emergence of new variants and transient decreases in influenza activity corresponded to the timing of school holidays. Real-time estimates demonstrated good agreement with those produced at the end of the season, with significant overlap of the 95% credible intervals. Our findings demonstrate how statistical methods can be implemented in real time to synthesise noisy case time-series data into interpretable trends (including uncertainty), enabling quantification of the strength of evidence for whether epidemic activity is increasing, stable or declining. Our real-time outputs were reported weekly to the Department of Health, Victoria during June-September 2024, complementing other routine surveillance indicators.