Reinforcement Learning Enhances the Experts: Large-scale COVID-19 Vaccine Allocation with Multi-factor Contact Network
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
; : 4684-4694, 2022.
Article
in English
| Scopus | ID: covidwho-2020405
ABSTRACT
In the fight against the COVID-19 pandemic, vaccines are the most critical resource but are still in short supply around the world. Therefore, efficient vaccine allocation strategies are urgently called for, especially in large-scale metropolis where uneven health risk is manifested in nearby neighborhoods. However, there exist several key challenges in solving this problem:
(1) great complexity in the large scale scenario adds to the difficulty in experts' vaccine allocation decision making;(2) heterogeneous information from all aspects in the metropolis' contact network makes information utilization difficult in decision making;(3) when utilizing the strong decision-making ability of reinforcement learning (RL) to solve the problem, poor explainability limits the credibility of the RL strategies. In this paper, we propose a reinforcement learning enhanced experts method. We deal with the great complexity via a specially designed algorithm aggregating blocks in the metropolis into communities and we hierarchically integrate RL among the communities and experts solution within each community. We design a self-supervised contact network representation algorithm to fuse the heterogeneous information for efficient vaccine allocation decision making. We conduct extensive experiments in three metropolis with real-world data and prove that our method outperforms the best baseline, reducing 9.01% infections and 12.27% deaths.We further demonstrate the explainability of the RL model, adding to its credibility and also enlightening the experts in turn. © 2022 Owner/Author.
covid-19 pandemic; model explainability; reinforcement learning; self-supervised representation learning; vaccine allocation.; Complex networks; Decision making; Health risks; Allocation decision; Contact networks; Decisions makings; Heterogeneous information; Large-scales; Reinforcement learnings; Vaccines
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Topics:
Vaccines
Language:
English
Journal:
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Year:
2022
Document Type:
Article
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