The Journal of infectious diseases · 2024
Impact of Subgroup Distribution on Seasonality of Human Respiratory Syncytial Virus: A Global Systematic Analysis.
Систематический обзорСтатьяНазвание и аннотация: Перевод в очереди
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Deng Shuyu, Guo Ling, Cohen Cheryl, Meijer Adam, Moyes Jocelyn, Pasittungkul Siripat, Poovorawan Yong, Teirlinck Anne, van Boven Michiel, Wanlapakorn Nasamon, Wolter Nicole, Paget John, Nair Harish, Li You, Respiratory Virus Global Epidemiology Network and the PROMISE Investigators
Аннотация
BACKGROUND: Previous studies reported inconsistent findings regarding the association between respiratory syncytial virus (RSV) subgroup distribution and timing of RSV season. We aimed to further understand the association by conducting a global-level systematic analysis.
METHODS: We compiled published data on RSV seasonality through a systematic literature review, and unpublished data shared by international collaborators. Using annual cumulative proportion (ACP) of RSV-positive cases, we defined RSV season onset and offset as ACP reaching 10% and 90%, respectively. Linear regression models accounting for meteorological factors were constructed to analyze the association of proportion of RSV-A with the corresponding RSV season onset and offset.
RESULTS: We included 36 study sites from 20 countries, providing data for 179 study-years in 1995-2019. Globally, RSV subgroup distribution was not significantly associated with RSV season onset or offset globally, except for RSV season offset in the tropics in 1 model, possibly by chance. Models that included RSV subgroup distribution and meteorological factors explained only 2%-4% of the variations in timing of RSV season.
CONCLUSIONS: Year-on-year variations in RSV season onset and offset are not well explained by RSV subgroup distribution or meteorological factors. Factors including population susceptibility, mobility, and viral interference should be examined in future studies.
METHODS: We compiled published data on RSV seasonality through a systematic literature review, and unpublished data shared by international collaborators. Using annual cumulative proportion (ACP) of RSV-positive cases, we defined RSV season onset and offset as ACP reaching 10% and 90%, respectively. Linear regression models accounting for meteorological factors were constructed to analyze the association of proportion of RSV-A with the corresponding RSV season onset and offset.
RESULTS: We included 36 study sites from 20 countries, providing data for 179 study-years in 1995-2019. Globally, RSV subgroup distribution was not significantly associated with RSV season onset or offset globally, except for RSV season offset in the tropics in 1 model, possibly by chance. Models that included RSV subgroup distribution and meteorological factors explained only 2%-4% of the variations in timing of RSV season.
CONCLUSIONS: Year-on-year variations in RSV season onset and offset are not well explained by RSV subgroup distribution or meteorological factors. Factors including population susceptibility, mobility, and viral interference should be examined in future studies.