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COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model.
K Stuckey1, P K Newton2,3
1Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles CA 90089-1191, United States of America.
Physica D. Nonlinear Phenomena
|December 21, 2022
Summary
This study models COVID-19 vaccine uptake using game theory and reinforcement learning. Optimized incentive programs can improve vaccine adoption, but benefits diminish above certain thresholds.
Area of Science:
- Epidemiology
- Behavioral Economics
- Computational Social Science
Background:
- COVID-19 vaccine uptake is crucial for public health.
- Heterogeneous population responses (adopters vs. hesitant) influence vaccine coverage.
- Understanding the dynamics of vaccine hesitancy is key to effective public health strategies.
Purpose of the Study:
- To model COVID-19 vaccine uptake as a dynamic game between adopters and hesitant individuals.
- To investigate the impact of dynamic incentive schedules on vaccine adoption rates.
- To determine optimal incentive strategies, including timing and magnitude, for maximizing vaccine uptake.
Main Methods:
- Utilized reinforcement learning and replicator dynamics to model population behavior.
- Estimated payoff matrices from Center for Disease Control (CDC) data.
- Applied optimal control theory to design and evaluate incentive schedules.
Main Results:
- Identified the interaction between vaccine adopters and hesitant individuals as a Hawk-Dove evolutionary game.
- Demonstrated that well-timed and designed incentive programs can increase vaccine uptake by shifting the Nash equilibrium.
- Found diminishing returns for incentive sizes exceeding a specific threshold.
Conclusions:
- Dynamic incentive programs offer a viable strategy to enhance COVID-19 vaccine uptake in large populations.
- The effectiveness of incentives is dependent on careful design, timing, and magnitude.
- There is a limit to the efficacy of incentives, with significant diminishing returns observed at higher levels.
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