Optimizing Vaccination Incentives to Prevent Disease Outbreaks


This award will contribute to the advancement of the national health and welfare by designing effective mechanisms to improve the vaccination rate for communicable diseases. Vaccination is one of the most effective public health interventions to prevent, control, and eliminate disease. The objective of this research is to identify optimal vaccination incentives given resource constraints to prevent disease outbreaks in a population. Barriers to high vaccination coverage include vaccine shortages, cost, lack of universal vaccination records, complexity of adherence, misinformation, and personal and cultural beliefs. This research will have broad impact on millions of individuals by providing policy makers with tools to promote vaccination uptake through sequential intervention design and effective utilization of healthcare resources to improve population health. The interdisciplinary nature of this research will provide students with diversified education and training. The PIs will develop an integrated modeling framework that combines incentive mechanism design via a game-theoretic approach, social network influencer identification, and agent-based simulation of large population infection networks. The research will advance multi-fidelity simulation optimization methods and apply them to optimize vaccination incentive policies to achieve herd immunity using the agent-based simulation. Incentives may include financial rewards to all individuals seeking vaccination, cost sharing for medical care, and/or incentives to social influencers via education outreach. Urban and rural use-cases will be analyzed to provide insights into controlling and eliminating the measles in the U.S. Going beyond the U.S. setting, the modeling framework can be generalized to low- and middle-income countries. The team will disseminate results from this work through publications in top journals, conferences across disciplines, and targeted public health agencies.