Abstract
Respiratory syncytial virus (RSV) causes respiratory illness in young children and is most commonly associated with bronchiolitis. RSV typically occurs as annual or biennial winter epidemics in temperate regions, with less pronounced seasonality in the tropics. We sought to characterise and compare the seasonality of RSV and bronchiolitis in temperate and tropical Western Australia. We examined over 13 years of RSV laboratory identifications and bronchiolitis hospitalisations in children, using an extensive linked dataset from Western Australia. We applied mathematical time series analyses to identify the dominant seasonal cycle, and changes in epidemic size and timing over this period. Both the RSV and bronchiolitis data showed clear winter epidemic peaks in July or August in the southern Western Australia regions, but less identifiable seasonality in the northern regions. Use of complex demodulation proved very effective at comparing disease epidemics. The timing of RSV and bronchiolitis epidemics coincided well, but the size of the epidemics differed, with more consistent peak sizes for bronchiolitis than for RSV. Our results show that bronchiolitis hospitalisations are a reasonable proxy for the timing of RSV detections, but may not fully capture the magnitude of RSV epidemics.
Highlights
Respiratory syncytial virus (RSV) is the most common cause of severe respiratory illness in young children
The descriptive analysis showed that most bronchiolitis hospitalisations were in children less than two years of age, while 73–83% of RSV diagnoses were in children aged less than two years
Our study enabled us to characterise the seasonal patterns of RSV and bronchiolitis in the different climatic zones of Western Australia
Summary
Respiratory syncytial virus (RSV) is the most common cause of severe respiratory illness in young children. RSV is highly seasonal, with annual or biennial (two-yearly) epidemics, while the epidemiology of RSV in the tropics is less well understood (Weber et al, 1998; Paynter et al, 2014). We use complex demodulation, a time series approach that allows us to visualise both the timing and size of RSV epidemics, and to detect shifts in epidemic behaviour over time. Complex demodulation has previously been applied to the analysis of geomagnetic data (Kingan et al, 1980) and individuallevel health data such as cardiovascular rhythms (Hayano et al, 1993; Shin et al, 1989; Kondo et al, 2014; Sasai et al, 2013), but to our knowledge has not yet been applied to population-level epidemiological data, except in the analysis of suicide data (Nader et al, 2011)
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