The Impact of Neglecting Vaccine Unwillingness in Epidemiology Models

TL;DR

This paper examines how ignoring vaccine unwillingness in epidemiological models leads to significant errors in predicting disease dynamics, especially over long-term and epidemic time scales, and explores ways to improve model accuracy.

Contribution

It highlights the importance of incorporating vaccine unwillingness into models and compares different methods to reduce modeling errors.

Findings

Neglecting vaccine unwillingness causes large errors in long-term equilibrium predictions.

Errors remain significant for less infectious diseases and slow vaccination programs during epidemic waves.

Adjusting the vaccination rate constant alone does not fully mitigate the errors caused by ignoring unwillingness.

Abstract

With significant population fractions in many societies who refuse vaccines, it is important to reconsider how vaccination is incorporated into compartmental epidemiology models. It is still most common to apply the vaccination rate to the entire class of susceptibles, rather than to use the more realistic assumption that the vaccination rate function should depend only on the population of susceptibles who are willing and able to receive a vaccination. This study uses a simple generic disease model to address two questions: (1) How much error is introduced in key model outcomes by neglecting vaccine unwillingness?, and (2) Can the error be reduced by incorporating vaccine unwillingness into the vaccination rate constant rather than the rate diagram? The answers depend greatly on the time scale of interest. For the endemic time scale, where longterm behavior is studied with equilibrium point analysis, the error in neglecting unwillingess is large and cannot be improved upon by decreasing the vaccination rate constant. For the epidemic time scale, where the first big epidemic wave is studied with numerical simulations, the error can still be significant, particularly for diseases that are relatively less infectious and vaccination programs that are relatively slow.