Application of a forecasting model to mitigate the consequences of unexpected RSV surge: Experience from the post-COVID-19 2021/22 winter season in a major metropolitan centre, Lyon, France

Jean Sebastien Casalegno, Samantha Bents, John Paget, Yves Gillet, Dominique Ploin, Etienne Javouhey, Bruno Lina, Florence Morfin, Bryan T. Grenfell, Rachel E. Baker

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background The emergence of COVID-19 triggered the massive implementation of non-pharmaceutical interventions (NPI) which impacted the circulation of respiratory syncytial virus (RSV) during the 2020/2021 season. Methods A time-series susceptible-infected-recovered (TSIR) model was used early September 2021 to forecast the implications of this disruption on the future 2021/2022 RSV epidemic in Lyon urban population. Results When compared to observed hospital-confirmed cases, the model successfully captured the early start, peak timing, and end of the 2021/2022 RSV epidemic. These simulations, added to other streams of surveillance data, shared and discussed among the local field experts were of great value to mitigate the consequences of this atypical RSV outbreak on our hospital paediatric department. Conclusions TSIR model, fitted to local hospital data covering large urban areas, can produce plausible post-COVID-19 RSV simulations.

Original languageEnglish (US)
Article number04007
JournalJournal of Global Health
Volume13
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Public Health, Environmental and Occupational Health
  • Health Policy

Fingerprint

Dive into the research topics of 'Application of a forecasting model to mitigate the consequences of unexpected RSV surge: Experience from the post-COVID-19 2021/22 winter season in a major metropolitan centre, Lyon, France'. Together they form a unique fingerprint.

Cite this