Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic.

ABSTRACT: We present a formal mathematical modeling framework for a multi-agent sequential decision problem during an epidemic. The problem is formulated as a collaboration between a vaccination agent and learning agent to allocate stockpiles of vaccines and tests to a set of zones under various types of uncertainty. The model is able to capture passive information processes and maintain beliefs over the uncertain state of the world. We designed a parameterized direct lookahead approximation which is robust and scalable under different scenarios, resource scarcity, and beliefs about the environment. We design a test allocation policy designed to capture the value of information and demonstrate that it outperforms other learning policies when there is an extreme shortage of resources (information is

SUBMITTER: Thul L 

PROVIDER: S-EPMC8580866 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature