Understanding RSV Resurgence Following COVID-19 in Ontario, Canada: Evaluating the Roles of Contact Patterns and Maternal Immunity
- 1Public Health Ontario
Abstract
BackgroundRespiratory syncytial virus (RSV) remains a leading cause of hospitalization in infancy, with severe outcomes influenced by both contact patterns and passive immunity. Non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic suppressed RSV circulation and reduced opportunities for maternal immune boosting, potentially altering protection among newborns. We evaluated whether incorporating time-varying maternal immunity improves the ability of an age-structured transmission model to predict post-pandemic RSV hospitalization patterns in infants.
MethodsWe analyzed population-based RSV hospitalizations among Ontario (Canada) infants (<1 year) from July 2, 2017 to June 25, 2024, using linked administrative databases. A deterministic compartmental model across seven age classes was calibrated against pre-pandemic data using Latin Hypercube Sampling. We compared a model incorporating time-varying contact rates alone against a specification that additionally included time-varying maternal immunity.
ResultsBoth specifications accurately reproduced pre-pandemic seasonality and macro-level post-pandemic resurgence features. The constant maternal immunity model showed slightly better accuracy in capturing the 2021/22 peak compared to the time-varying maternal immunity specification. However, both qualitatively captured the continued near-absence of RSV and the observed peak was captured within the 95% credible intervals. While both models precisely captured the timing and overwhelming surge of admissions that occurred in 2022/23, they failed to capture the premature peak timing and magnitude in 2023/24.
ConclusionsIncorporating time-varying maternal immunity did not improve model accuracy post-pandemic. While maternal protection is essential for evaluating infant immunizations, population-level contact shifts primarily shaped post-pandemic RSV seasonality, indicating that models must account for these mechanisms of RSV transmission dynamics.
SummaryUsing population-based Ontario hospitalization data, we evaluated whether incorporating time-varying maternal immunity improves an age-structured transmission model s ability to reproduce infant RSV disease patterns. While maternal immunity did impact post-pandemic RSV resurgences, we found that population-level contact pattern shifts were primarily responsible.
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BibTeX
@misc{parpia2026understanding,
title = {{Understanding RSV Resurgence Following COVID-19 in Ontario, Canada: Evaluating the Roles of Contact Patterns and Maternal Immunity}},
author = {A. Parpia and J. Wright and A. Gharouni and N. Thampi and T. Fitzpatrick},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.28.26361657},
url = {https://www.medrxiv.org/content/10.64898/2026.08.28.26361657v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Understanding RSV Resurgence Following COVID-19 in Ontario, Canada: Evaluating the Roles of Contact Patterns and Maternal Immunity
AU - A. Parpia
AU - J. Wright
AU - A. Gharouni
AU - N. Thampi
AU - T. Fitzpatrick
PY - 2026
DA - 2026/08/28
PB - medRxiv
DO - 10.64898/2026.08.28.26361657
UR - https://www.medrxiv.org/content/10.64898/2026.08.28.26361657v1
AB - BackgroundRespiratory syncytial virus (RSV) remains a leading cause of hospitalization in infancy, with severe outcomes influenced by both contact patterns and passive immunity. Non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic suppressed RSV circulation and reduced opportunities for maternal immune boosting, potentially altering protection among newborns. We evaluated whether incorporating time-varying maternal immunity improves the ability of an age-structured transmission model to predict post-pandemic RSV hospitalization patterns in infants. MethodsWe analyzed population-based RSV hospitalizations among Ontario (Canada) infants (<1 year) from July 2, 2017 to June 25, 2024, using linked administrative databases. A deterministic compartmental model across seven age classes was calibrated against pre-pandemic data using Latin Hypercube Sampling. We compared a model incorporating time-varying contact rates alone against a specification that additionally included time-varying maternal immunity. ResultsBoth specifications accurately reproduced pre-pandemic seasonality and macro-level post-pandemic resurgence features. The constant maternal immunity model showed slightly better accuracy in capturing the 2021/22 peak compared to the time-varying maternal immunity specification. However, both qualitatively captured the continued near-absence of RSV and the observed peak was captured within the 95% credible intervals. While both models precisely captured the timing and overwhelming surge of admissions that occurred in 2022/23, they failed to capture the premature peak timing and magnitude in 2023/24. ConclusionsIncorporating time-varying maternal immunity did not improve model accuracy post-pandemic. While maternal protection is essential for evaluating infant immunizations, population-level contact shifts primarily shaped post-pandemic RSV seasonality, indicating that models must account for these mechanisms of RSV transmission dynamics. SummaryUsing population-based Ontario hospitalization data, we evaluated whether incorporating time-varying maternal immunity improves an age-structured transmission model s ability to reproduce infant RSV disease patterns. While maternal immunity did impact post-pandemic RSV resurgences, we found that population-level contact pattern shifts were primarily responsible.
ER -