Using COVID-19 pandemic perturbation to model RSV-hMPV interactions and potential implications under RSV...

Area of Science:

• Virology
• Epidemiology
• Mathematical Modeling

Background:

• Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) are significant causes of acute respiratory infections.
• The interaction mechanisms between RSV and hMPV remain largely uncharacterized.

Purpose of the Study:

• To quantify the likelihood of RSV-hMPV interactions using population-level surveillance data.
• To investigate the potential impact of RSV interventions on hMPV burden.

Main Methods:

• Utilized a mathematical model to analyze surveillance data from Scotland, Korea, and Canada.
• Employed a two-pathogen transmission model to assess interaction dynamics.
• Validated the model using post-pandemic RSV-hMPV rebound data.

Main Results:

• Observed that hMPV outbreaks consistently lag RSV outbreaks, suggesting an interaction.
• Demonstrated that a negative effect of RSV on hMPV transmissibility explains observed epidemiological patterns.
• The model incorporating interactions showed superior predictive accuracy compared to an independent pathogen model.

Conclusions:

• RSV infection appears to negatively impact hMPV transmissibility, influencing outbreak timing and dynamics.
• RSV interventions may alter hMPV peak timing and magnitude, necessitating further investigation.
• The study provides a framework for detecting RSV-hMPV interactions at the population level.