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. 2025 Jan 29;23(1):55.
doi: 10.1186/s12916-025-03888-4.

Understanding the local-level variations in seasonality of human respiratory syncytial virus infection: a systematic analysis

Sheng Ye #  1 , Shuyu Deng #  1 , Yumeng Miao  1 , David Torres-Fernandez  2   3 , Quique Bassat  2   3   4   5   6   7 , Xin Wang  8   9 , You Li  10   11   12
Affiliations

Understanding the local-level variations in seasonality of human respiratory syncytial virus infection: a systematic analysis

Sheng Ye et al. BMC Med. .

Abstract

Background: While previous reports characterised global and regional variations in RSV seasonality, less is known about local variations in RSV seasonal characteristics. This study aimed to understand the local-level variations in RSV seasonality and to explore the role of geographical, meteorological, and socio-demographic factors in explaining these variations.

Methods: We conducted a systematic literature review to identify published studies reporting data on local-level RSV season onset, offset, or duration for at least two local sites. In addition, we included three datasets of RSV activity from Japan, Spain, and Scotland with available site-specific data. RSV season onset, offset, and duration were defined using the annual cumulative proportion method. We estimated between-site variations within a region using the earliest onset, the earliest offset, and the shortest duration of RSV season of that region as the references and synthesised the variations across regions by a multi-level mixed-effects meta-analysis. Using the three datasets from Japan, Spain and Scotland, we applied linear regression models with clustered standard errors to explore the association of geographical, meteorological, and socio-demographic factors with the season onset and offset, respectively.

Results: We included 7 published studies identified from the systematic literature search. With the additional 3 datasets, these data sources covered 888,447 RSV-positive cases from 101 local study sites during 1995 to 2020. Local-level variations in RSV season within a region were estimated to be 6 weeks (41 days, 95% CI: 25-57) for season onset, 5 weeks (32 days, 13-50) for season offset, and 6 weeks (40 days, 20-59) for season duration, with substantial differences across years. Multiple factors, such as temperature, relative humidity, wind speed, annual household income, population size, latitude, and longitude, could jointly explain 66% to 84% and 35% to 49% of the variations in season onset and offset, respectively, although their individual effects varied by individual regions.

Conclusions: Local-level variations in RSV season onset could be as much as 6 weeks, which could be influenced by meteorological, geographical, and socio-demographic factors. The reported variations in this study could have important implications for local-level healthcare resources planning and immunisation strategy.

Trial registration: PROSPERO CRD42023482432.

Keywords: Meteorological factors; Respiratory syncytial virus; Seasonality; Socio-demographic factors.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. As a systematic analysis of previously published data, this work did not require ethical approval. Consent for publication: All authors have read and agreed to the published version of the manuscript. Competing interests: YL reports grants by GSK, MSD, and WHO; and personal fees from Pfizer, MSD, and WHO, outside the submitted work. All other authors declared no competing interests.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram of the study inclusion
Fig. 2
Fig. 2
Forest plots of variations in local RSV onset across study regions. A random-effects (RE) model was used for the meta-analysis. Results are shown as differences in days (95% CI). The squares represent the estimated local day-difference in RSV onset, with the size of each square proportional to the weight of the study. The error bars represent the corresponding 95% confidence intervals (CI). The centre of the diamond indicates the overall estimated day-difference across all studies, with the width of the diamond representing the pooled 95% CI
Fig. 3
Fig. 3
Forest plots of variations in local RSV offset across study regions. A random-effects (RE) model was used for the meta-analysis. Results are shown as differences in days (95% CI). The squares represent the estimated local day-difference in RSV onset, with the size of each square proportional to the weight of the study. The error bars represent the corresponding 95% confidence intervals (CI). The centre of the diamond indicates the overall estimated day-difference across all studies, with the width of the diamond representing the pooled 95% CI
Fig. 4
Fig. 4
Forest plots of variations in local RSV duration across study regions. A random-effects (RE) model was used for the meta-analysis. Results are shown as differences in days (95% CI). The squares represent the estimated local day-difference in RSV onset, with the size of each square proportional to the weight of the study. The error bars represent the corresponding 95% confidence intervals (CI). The centre of the diamond indicates the overall estimated day-difference across all studies, with the width of the diamond representing the pooled 95% CI
See this image and copyright information in PMC

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