Using COVID-19 pandemic perturbation to model RSV-hMPV interactions and potential implications under RSV interventions
Nature Communications·
- DOI
- 10.1038/s41467-025-62358-w
- PMID
- 40770182
- PMCID
- PMC12328645
- OpenAlex
- W4413018807
- Study type
- Mathematical modelling
- Publisher
- Springer Science and Business Media LLC
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Findings provide a modeling foundation for anticipating possible hMPV dynamics when RSV immunization strategies are deployed.
Structured evidence summary
Research question
The study investigates potential population-level interactions between RSV and hMPV and whether RSV medical interventions could alter hMPV burden.
Study design
Mathematical transmission modeling applied to multi-country surveillance data, with an out-of-sample test against post-pandemic rebound dynamics.
Population and setting
Population-level surveillance data from Scotland, South Korea, and three regions of Canada.
Main findings
Annual hMPV outbreaks lagged RSV outbreaks by up to 18 weeks in the studied regions, with two Canadian regions showing out-of-phase biennial patterns. A model incorporating a negative effect of RSV infection on hMPV transmissibility fit these dynamics and better predicted post-pandemic rebound than an independent-pathogen model. The model further suggests hMPV peak timing and magnitude may shift under RSV interventions.
Public-health relevance
Findings provide a modeling foundation for anticipating possible hMPV dynamics when RSV immunization strategies are deployed.
Important limitations
The authors note that the model oversimplifies important complexities about interaction mechanisms. This summary is limited to the supplied single-article abstract and metadata, and the original paper is required for decision-grade interpretation.
GIDS interpretation
This modeling study may aid in contextualizing respiratory virus surveillance interpretation, but the supplied abstract alone does not establish a connection to any live surveillance signal.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 13 auditable classifier relationships to diseases, places, topics, and study design.