# Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling

arXiv: https://arxiv.org/abs/2109.10004 · HTML: https://mlsanity.com/paper/2109.10004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 6 of 6)

- [Where to locate COVID-19 mass vaccination facilities?](https://mlsanity.com/paper/2102.07309/) (2021)
- [Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints](https://mlsanity.com/paper/1908.07964/) (2019)
- [Determination of an Optimal Control Strategy for Vaccine Administration in COVID-19 Pandemic Treatment](https://mlsanity.com/paper/2004.07397/) (2020)
- [Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic](https://mlsanity.com/paper/2101.01204/) (2021)
- [Optimizing vaccine distribution networks in low and middle-income countries](https://mlsanity.com/paper/1907.13434/) (2019)
- [Pricing the COVID-19 Vaccine: A Mathematical Approach](https://mlsanity.com/paper/2101.03234/) (2020)

## Abstract (arXiv)

The optimal allocation of vaccines to population subgroups over time is a challenging health care management problem. In the context of a pandemic, the interaction between vaccination policies adopted by multiple agents and the cooperation (or lack thereof) creates a complex environment that affects the global transmission dynamics of the disease. In this study, we take the perspective of decision-making agents that aim to minimize the size of their susceptible populations and must allocate vaccine under limited supply. We assume that vaccine efficiency rates are unknown to agents and we propose an optimization policy based on Thompson sampling to learn mean vaccine efficiency rates over time. Furthermore, we develop a budget-balanced resource sharing mechanism to promote cooperation among agents. We apply the proposed framework to the COVID-19 pandemic. We use a raster model of the world where agents represent the main countries worldwide and interact in a global mobility network to generate multiple problem instances. Our numerical results show that the proposed vaccine allocation policy achieves a larger reduction in the number of susceptible individuals, infections and deaths globally compared to a population-based policy. In addition, we show that, under a fixed global vaccine allocation budget, most countries can reduce their national number of infections and deaths by sharing their budget with countries with which they have a relatively high mobility exchange. The proposed framework can be used to improve policy-making in health care management by national and global health authorities.