1. Vaccination Diffusion and Incentive: Empirical Analysis of the US State of Michigan.
- Author
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Kim H and Rao VR
- Subjects
- Cities, Diffusion, Humans, Michigan, Motivation, Vaccination
- Abstract
Vaccination is the only way to reach herd immunity and help people return to normal life. However, vaccination rollouts may not be as fast as expected in some regions due to individuals' vaccination hesitation. For this reason, in Detroit, Michigan, the city government has offered a $50 prepaid card to people who entice city residents to visit vaccination sites. This study examined vaccination rates in the US using Detroit, Michigan, as the setting. It sought to address two issues. First, we analyzed the vaccination diffusion process to predict whether any region would reach a vaccination completion level that ensures herd immunity. Second, we examined a natural experiment involving a vaccination incentive scheme in Detroit and discovered its causal inference. We collected weekly vaccination data and demographic Census data from the state of Michigan and employed the Bass model to study vaccination diffusion. Also, we used a synthetic control method to evaluate the causal inference of a vaccination incentive scheme utilized in Detroit. The results showed that many Michigan counties-as well as the city of Detroit-would not reach herd immunity given the progress of vaccination efforts. Also, we found that Detroit's incentive scheme indeed increased the weekly vaccination rate by 44.19% for the first dose (from 0.86 to 1.25%) but was ineffective in augmenting the rate of the second dose. The implications are valuable for policy makers to implement vaccination incentive schemes to boost vaccination rates in geographical areas where such rates remain inadequate for achieving herd immunity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kim and Rao.)
- Published
- 2021
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