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Sequential vaccine allocation with delayed feedback
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Xiao, Yichen, Ou, Han-Ching, Chen, Haipeng, Nguyen, Van Thieu and Tran-Thanh, Long (2022) Sequential vaccine allocation with delayed feedback. In: International Joint Conference on Artificial Intelligence (IJCAI 2022), Vienna, Austria, 25-29 Jul 2022. Published in: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022) pp. 5199-5205. doi:10.24963/ijcai.2022/722
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WRAP-Sequential-vaccine-allocation-with-delayed-feedback-Tran-Thanh-22.pdf - Accepted Version - Requires a PDF viewer. Download (527Kb) | Preview |
Official URL: https://doi.org/10.24963/ijcai.2022/722
Abstract
In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies. In particular, we show that with our allocation method, we can reduce the number of required vaccination by atleast50%in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%. We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it re-duces uncertainty, hence results in better and more interpretable communication)
Item Type: | Conference Item (Paper) | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Vaccination -- Mathematical models, COVID-19 (Disease) -- Vaccination -- Management -- Mathematical models., Neural networks (Computer science) | ||||||
Journal or Publication Title: | Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022) | ||||||
Publisher: | IJCAI | ||||||
Official Date: | 2022 | ||||||
Dates: |
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Page Range: | pp. 5199-5205 | ||||||
DOI: | 10.24963/ijcai.2022/722 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | International Joint Conferences on Artificial Intelligence | ||||||
Date of first compliant deposit: | 11 July 2022 | ||||||
Date of first compliant Open Access: | 12 July 2022 | ||||||
Conference Paper Type: | Paper | ||||||
Title of Event: | International Joint Conference on Artificial Intelligence (IJCAI 2022) | ||||||
Type of Event: | Conference | ||||||
Location of Event: | Vienna, Austria | ||||||
Date(s) of Event: | 25-29 Jul 2022 | ||||||
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Open Access Version: |
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