Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling
David Rey, Ahmed W Hammad, Meead Saberi

TL;DR
This paper introduces a Thompson sampling-based vaccine allocation policy and a budget sharing mechanism to optimize global vaccination efforts during a pandemic, demonstrating improved health outcomes through cooperation.
Contribution
It presents a novel framework combining Thompson sampling for vaccine efficiency learning with a resource sharing mechanism for cooperative policy-making.
Findings
The proposed policy reduces susceptible individuals, infections, and deaths more effectively than population-based policies.
Most countries can lower their infections and deaths by sharing vaccine budgets based on mobility exchange.
The framework enhances global health outcomes through cooperative vaccine allocation strategies.
Abstract
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…
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Taxonomy
TopicsCOVID-19 epidemiological studies · Influenza Virus Research Studies · Vaccine Coverage and Hesitancy
