Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants

Respiratory Virus Global Epidemiology Network; PROMISE investigators

doi:10.1038/s41467-025-61400-1

Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants

Respiratory Virus Global Epidemiology Network
, PROMISE investigators

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.

Original language	English
Article number	6109
Journal	Nature Communications
Volume	16
Issue number	1
Early online date	3 Jul 2025
DOIs	https://doi.org/10.1038/s41467-025-61400-1
Publication status	Published - Dec 2025

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10.1038/s41467-025-61400-1

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@article{7e4a86d60eee41f59310122837589987,

title = "Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants",

abstract = "Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.",

keywords = "Humans, Respiratory Syncytial Virus Infections/epidemiology, Hospitalization/statistics \& numerical data, Infant, Seasons, Bayes Theorem, Infant, Newborn, Respiratory Syncytial Virus, Human, Age Factors",

author = "\{Respiratory Virus Global Epidemiology Network\} and \{PROMISE investigators\} and Ling Guo and Sebastien Kenmoe and Fuyu Miyake and Alexandria Chung and Han Zhang and Teresa Bandeira and Caballero, \{Mauricio T.\} and Casalegno, \{Jean S{\'e}bastien\} and Rodrigo Fasce and Chakhunashvili Giorgi and Terho Heikkinen and Huang, \{Q. Sue\} and Katama, \{Esther Nyadzua\} and Keck, \{James W.\} and Enmei Liu and Josko Markic and Moore, \{Hannah C.\} and Jocelyn Moyes and Rath, \{Barbara A.\} and Candice Romero and Qianli Wang and Marta Werner and Yung, \{Chee Fu\} and Roger Hernandez and Isabel Bazan and Giselle Soto and Heather Scobie and Sara Bressler and Christine Desnoyers and Alfonso Cepeda and Andres Bugue{\~n}o and Olga Lopez and Horvat Come and Dominique Ploin and Ivan Rodriguez and Paula Enriquez and Obermeier, \{Patrick E.\} and Sibongile Walaza and Cheryl Cohen and Nicole Wolter and Huong Le and Belaynew Taye and Mohinder Sarna and Dina Mrcela and Yu Deng and Na Zang and Luo Ren and Agustus Kea and Martin Mutunga and Tim Wood and Olgha, \{Tarkhan Mouravi\} and Zakhashvili Khatuna and Karseladze Irakli and Florence Morfin-Sherpa and Julia Dvorkin and Ros{\'a}rio Barreto and Zakhar Shchomak and Harish Nair and You Li and Eva Molero and Rolf Kramer and Jenny Hendrix and Jim Janimak and Elizabeth Begier and Veena Kumar and Andrew Pollard and Peter Openshaw and Louis Bont and Hanna Nohynek and Xin Wang",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2025.",

year = "2025",

month = dec,

doi = "10.1038/s41467-025-61400-1",

language = "English",

volume = "16",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Research",

number = "1",

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TY - JOUR

T1 - Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants

AU - Respiratory Virus Global Epidemiology Network

AU - PROMISE investigators

AU - Guo, Ling

AU - Kenmoe, Sebastien

AU - Miyake, Fuyu

AU - Chung, Alexandria

AU - Zhang, Han

AU - Bandeira, Teresa

AU - Caballero, Mauricio T.

AU - Casalegno, Jean Sébastien

AU - Fasce, Rodrigo

AU - Giorgi, Chakhunashvili

AU - Heikkinen, Terho

AU - Huang, Q. Sue

AU - Katama, Esther Nyadzua

AU - Keck, James W.

AU - Liu, Enmei

AU - Markic, Josko

AU - Moore, Hannah C.

AU - Moyes, Jocelyn

AU - Rath, Barbara A.

AU - Romero, Candice

AU - Wang, Qianli

AU - Werner, Marta

AU - Yung, Chee Fu

AU - Hernandez, Roger

AU - Bazan, Isabel

AU - Soto, Giselle

AU - Scobie, Heather

AU - Bressler, Sara

AU - Desnoyers, Christine

AU - Cepeda, Alfonso

AU - Bugueño, Andres

AU - Lopez, Olga

AU - Come, Horvat

AU - Ploin, Dominique

AU - Rodriguez, Ivan

AU - Enriquez, Paula

AU - Obermeier, Patrick E.

AU - Walaza, Sibongile

AU - Cohen, Cheryl

AU - Wolter, Nicole

AU - Le, Huong

AU - Taye, Belaynew

AU - Sarna, Mohinder

AU - Mrcela, Dina

AU - Deng, Yu

AU - Zang, Na

AU - Ren, Luo

AU - Kea, Agustus

AU - Mutunga, Martin

AU - Wood, Tim

AU - Olgha, Tarkhan Mouravi

AU - Khatuna, Zakhashvili

AU - Irakli, Karseladze

AU - Morfin-Sherpa, Florence

AU - Dvorkin, Julia

AU - Barreto, Rosário

AU - Shchomak, Zakhar

AU - Nair, Harish

AU - Li, You

AU - Molero, Eva

AU - Kramer, Rolf

AU - Hendrix, Jenny

AU - Janimak, Jim

AU - Begier, Elizabeth

AU - Kumar, Veena

AU - Pollard, Andrew

AU - Openshaw, Peter

AU - Bont, Louis

AU - Nohynek, Hanna

AU - Wang, Xin

PY - 2025/12

Y1 - 2025/12

N2 - Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.

AB - Understanding the distribution of respiratory syncytial virus (RSV) disease burden by more granular age bands in infants is necessary for optimising infant RSV immunisation strategies. Using a Bayesian model, we synthesised published data from a systematic literature review and unpublished data shared by international collaborators for estimating the distribution of infant RSV hospitalisations by month of age. Based on local RSV seasonality data, we further developed and validated a web-based prediction tool for estimating infant RSV hospitalisation distribution by birth month. Although RSV hospitalisation burden mostly peaked at the second month of life and was concentrated in infants under six months globally, substantial variations were noted in the age distribution of RSV hospitalisation among infants born in different months. Passive immunisation strategies should ideally be tailored to the local RSV disease burden distribution by age and birth month to maximise their per-dose effectiveness before a universal immunisation can be achieved.

KW - Humans

KW - Respiratory Syncytial Virus Infections/epidemiology

KW - Hospitalization/statistics & numerical data

KW - Infant

KW - Seasons

KW - Bayes Theorem

KW - Infant, Newborn

KW - Respiratory Syncytial Virus, Human

KW - Age Factors

UR - https://www.scopus.com/pages/publications/105010547537

U2 - 10.1038/s41467-025-61400-1

DO - 10.1038/s41467-025-61400-1

M3 - Article

C2 - 40610449

SN - 2041-1723

VL - 16

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 6109

ER -

Respiratory syncytial virus hospitalisation by chronological month of age and by birth month in infants

Abstract

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