Publication date: Jul 01, 2026
Vaccination is widely used to control the spread of infectious diseases. However, there is substantial evidence suggesting that the perceived immunity from vaccine drives the decrease of risk-awareness among vaccinated individuals, which results in the paradoxical increase of transmission risk, consequently leads to the counter-intuitive outcomes in disease control in the case of low vaccine effectiveness. In this study, we propose a novel method to quantify the counter-intuitive effects of vaccination in a complex environment with integrated dynamic factors. To this end, we begin by linking shifts in risk awareness to human behavior changes, and subsequently, we couple the evolution dynamic of human behaviors with the disease transmission dynamic system by applying game theory within a multi-scale model, distinguishing between vaccinated and unvaccinated populations. We calibrate the proposed model using vaccination data and epidemic data for the Omicron variant of SARS-CoV-2, spanning from 23/12/2021 to 20/09/2022 with two epidemic waves in Tokyo Metropolis, Japan. The calibration results provide the quantitative evidence that the vaccinated group exhibits a slower rate of risk perception to adjust their behaviors and a higher mental threshold for behavioral changes needed to protect themselves against infection. The model estimates that if vaccinated individuals had maintained the same level of NPI adherence and behavioral responses to infection prevalence as unvaccinated individuals, the accumulative confirmed cases during the study period would have been reduced by approximately 41. 55% (although the exact proportion depends on the chosen time window). This finding highlights the importance of post-vaccination behavioral responses. Additionally, our analysis suggests an optimal vaccination coverage that minimizes accumulative infections by balancing the protective effects and the counter-intuitive effects of mass vaccination. Therefore, it’s of significant meaning to collect and disclose the epidemic data with vaccination status and timely evaluate the effective transmission among the population involving human behavior dynamics for designing the proper vaccination strategy, and improve the vaccine effectiveness and maintain high risk-awareness for vaccinated individuals would still be the key to flatten the epidemics.
| Concepts | Keywords |
|---|---|
| Covid | Behavioral change |
| Japan | Counter-intuitive effects |
| Slower | COVID-19 |
| Vaccines | Game theory |
| Multi-scale model | |
| Vaccination |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | MESH | infectious diseases |
| disease | MESH | infection |