Defining the seasonality of respiratory syncytial virus around the world: National and subnational surveillance data from 12 countries
Staadegaard, L., Caini, S., Wangchuk, S., Thapa, B., Almeida, W.A.F. de, Carvalho, F.C. de, Fasce, R.A., Bustos, P., Kyncl, J., Novakova, L., Caicedo, A.B., Mora Coloma, D.J. de, Meijer, A., Hooiveld, M., Huang, Q.S., Wood, T., Guiomar, R., Rodrigues, A.P., Lee, V.J.M., Ang, L.W., Cohen, C., Moyes, J., Larrauri, A., Delgado-Sanz, C., Demont, C., Bangert, M., Dückers, M., Summeren, J. van, Paget, J. Defining the seasonality of respiratory syncytial virus around the world: National and subnational surveillance data from 12 countries Journal of Infectious Diseases: 2021, p. 1-10
Respiratory syncytial virus (RSV) infections are one of the leading causes of lower respiratory tract infections and have a major burden on society. For prevention and control to be deployed effectively, an improved understanding of the seasonality of RSV is necessary.
The main objective of this study was to contribute to a better understanding of RSV seasonality by examining the GERi multi-country surveillance dataset.
RSV seasons were included in the analysis if they contained ≥100 cases. Seasonality was determined using the “average annual percentage” method. Analyses were performed at a subnational level for the United States and Brazil.
We included 601 425 RSV cases from 12 countries. Most temperate countries experienced RSV epidemics in the winter, with a median duration of 10–21 weeks. Not all epidemics fit this pattern in a consistent manner, with some occurring later or in an irregular manner. More variation in timing was observed in (sub)tropical countries, and we found substantial differences in seasonality at a subnational level. No association was found between the timing of the epidemic and the dominant RSV subtype.
Our findings suggest that geographical location or climatic characteristics cannot be used as a definitive predictor for the timing of RSV epidemics and highlight the need for (sub)national data collection and analysis.