Distinguishing vaccine efficacy and effectiveness.

Area of Science:

• Epidemiology
• Mathematical Modeling
• Vaccinology

Background:

• Mathematical models commonly equate vaccine efficacy with direct vaccine effectiveness.
• This assumption's validity in disease transmission and vaccination modeling remains unevaluated.
• Accurate modeling is crucial for predicting vaccine impact on individual protection and population disease control.

Purpose of the Study:

• To evaluate common vaccine effectiveness measures against individual protection and population disease control.
• To determine how vaccine-mediated attack rate reductions translate into various effectiveness metrics.
• To demonstrate correct parameterization of epidemiological models using clinical trial data.

Main Methods:

• Utilized dynamic epidemiological models for influenza and measles vaccines.
• Analyzed compartmental models to assess vaccine efficacy and population-level effectiveness.
• Examined the translation of reduced attack rates into direct, indirect, total, and overall vaccine effects.

Main Results:

• Standard parameterization of vaccine efficacy can underestimate vaccine impact, especially at low coverage.
• Vaccine effectiveness measures ('total', 'indirect', 'overall') increase with vaccination coverage.
• Methods for correctly calculating vaccine efficacy and effectiveness measures were established.

Conclusions:

• Current vaccine efficacy parameterization in models may underestimate real-world protective effects.
• Discrepancies exist between model predictions and actual population-level vaccination outcomes.
• Correct model parameterization from clinical data is essential for accurate vaccination impact assessment.