Projecting influenza vaccine effectiveness: A simulation study

Abstract

The impact of influenza vaccination is largely measured by estimating vaccine effectiveness (VE), which vary in different seasons. Strain mutations and waning immunity present two key mechanisms affecting VE. We sought to quantify the relative effect of these mechanisms by projecting VE and the reduction of illness due to vaccination. We developed a stochastic age-structured agent-based simulation model of influenza transmission dynamics to encapsulate intraseason waning of immunity post-vaccination, and mutation-induced antigenic distance between circulating strains and vaccine strains. Parameterizing the model with published estimates, we projected the temporal and overall VE during an epidemic season, and estimated the reduction of infection for high (70%) and low (30%) vaccine efficacies to reflect the levels of vaccine-induced protection in randomized control trials. Both temporal and overall VE decreased as the attack rate increased, with lower median values for epidemics starting with strains that were antigenically more distant from vaccine strains. We observed a higher rate of temporal decline with considerably lower median values of the overall VE in the presence of intraseason waning of immunity compared with only the antigenic distance effect. The highest benefit of vaccination in preventing influenza infection was achieved at moderate attack rates in the range of 6%-15%. The results show that even when VE is relatively low in the population and almost negligible for older age groups (i.e., 50+ years), vaccination can still prevent significant illness in high-risk individuals; thereby reducing healthcare resource utilization and economic burden. Our study indicates that early vaccination remains an important strategy for alleviating the burden of seasonal influenza. Policy discussions on optimal timing of vaccination to reduce the effect of intraseason waning of immunity should be considered in the context of strain mutations within the epidemic course.

Fig 1

Projected VE (a1-a5) and reduction of illness (b1-b5) in the antigenic distance model with an initial vaccine efficacy of 70% for calibrated attack rates in the absence of vaccination. Simulated scenarios correspond to the presence of circulating strains with different antigenic distance at the onset of epidemic. Boxplots represents the variation in projected VE and reduction of illness with median values shown by red circles.

Fig 2

Projected VE (a1-a5) and reduction of illness (b1-b5) in the antigenic distance model with an initial vaccine efficacy of 30% for calibrated attack rates in the absence of vaccination. Simulated scenarios correspond to the presence of circulating strains with different antigenic distance at the onset of epidemic. Boxplots represents the variation in projected VE and reduction of illness with median values shown by red circles.

Fig 3. Temporal median VE in the antigenic distance model with calibrated attack rates in the absence of vaccination.

Colour curves correspond to epidemics starting with strains of different antigenic distances in the range 0–0.04. Vaccine efficacy was set to 70% (a1-a6) and 30% (b1-b6).

Fig 4

Projected VE and reduction of illness in the waning immunity model with an initial vaccine efficacy of 70% (a,b) and 30% (c,d) for calibrated attack rates in the absence of vaccination. Boxplots represent the variation in projected VE and reduction of illness with median values shown by red circles.

Fig 5. Temporal median VE in the waning immunity model with calibrated attack rates in the absence of vaccination.

Vaccine efficacy was set to 70% (a1), and 30% (a2).