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VacSIM: Learning Effective Strategies for COVID-19 Vaccine Distribution using Reinforcement Learning
[article]
2021
arXiv
pre-print
A COVID-19 vaccine is our best bet for mitigating the ongoing onslaught of the pandemic. However, vaccine is also expected to be a limited resource. An optimal allocation strategy, especially in countries with access inequities and temporal separation of hot-spots, might be an effective way of halting the disease spread. We approach this problem by proposing a novel pipeline VacSIM that dovetails Deep Reinforcement Learning models into a Contextual Bandits approach for optimizing the
arXiv:2009.06602v3
fatcat:2yfa3xapyna5lnlryuq6237uae