Fig. 4: Model fit to RSV admissions to test different viral interference hypotheses.

A Model fit (blue curve) to weekly RSV data (black curve) from 1998 to 2016, assuming viral interference effects are equivalent across different periods (i.e., Model 1: \({\xi }_{s}={\xi }_{p}={\xi }_{{sp}}\); see S5 Table for estimated parameter values), assuming viral interference effects differ before and after the pandemic (i.e., Model 2: \({\xi }_{s}\), \({\xi }_{p}={\xi }_{{sp}}\); see S6 Table for estimated parameter values), or assuming viral interference effects differ across all three periods (i.e., Model 3: \({\xi }_{s}\), \({\xi }_{p}\), \({\xi }_{{sp}}\); see S7 Table for estimated parameter values). A 95% credible interval (CrI, shaded purple) was computed for the Model 3 using Latin Hypercube Sampling with Sampling-Importance-Resampling methods. Correlations between (B) the center of gravity (in weeks) and (C) the intensity of observed and predicted seasonal RSV epidemics (measured by the maximum weekly number of hospital admissions) from the best-fitting model—assuming viral interference effects differ across all three periods (Model 3)—were assessed using a two-sided Pearson correlation test (B: p-value = 9.2 \(\times\) \(1{0}^{-4}\), C: p-value = 2.1 \(\times\) \(1{0}^{-4}\)). Source data are provided as a Source Data file.