Deep Reinforcement Learning (DRL) Is Used to Optimize Vaccination Strategies and Resource Allocation
DOI:
https://doi.org/10.54097/see7fa88Keywords:
Deep reinforcement learning, vaccination strategy, resource allocation, optimization, simulation.Abstract
The purpose of this paper is to explore the application of deep reinforcement learning (DRL) in optimizing vaccination strategies and resource allocation. By constructing a DRL-based model, we aim to address the strategy selection and resource allocation problems encountered in the vaccination process. In this study, simulation experiments were used to verify the effectiveness of the model, and the model was compared with the existing methods. The results show that the DRL model can effectively improve the efficiency of vaccination and optimize the use of resources.
Downloads
References
S. Busha &İ. E. Büyüktahtakın, 《A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack》, J. Glob. Optim., 02,2024, doi: 10.1007/s10898-024-01364-6.
L. J. Oostveen, 《Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction》, Am. J. Roentgenol., Vol. 220, Issue 3, pp. 381–388, Mar 2023, doi: 10.2214/AJR.22.28319.
A. Hasanzadeh, et al. 《Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines》, Nano Today, vol 47, Issue 101665, 12-2022, yogurt: 10.1016/j.nantod.2022.101665.
《Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications | IEEE Journals & Magazine | IEEE Xplore》. https://ieeexplore.ieee.org/abstract/document/9321458
R. A. Khalil, N. Saeed, M. Masood, Y. M. Fard, M.-S. Alouini and T. Y. Al-Naffouri, 《Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications》, IEEE Internet Things J., Vol. 8, Issue 14, Pages 11016–11040, Jul 2021, doi: 10.1109/JIOT.2021.3051414.
S. Latif, et al. 《Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions》, Sensors, Vol 21, Issue 22, Art. Issue 22, Jan 2021, doi: 10.3390/s21227518.
D. N. Makau, C. Prieto, F. J. Martínez-Lobo, I. A. D. Paploski and K. VanderWaal, 《Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning》, Microbiol. Spectr., March 11, 20085-22, 1222, doi: 10.1128/spectrum.04085-22.
J. G. Faris, D. Orbidan, C. Wells, B. K. Petersen and K. G. Sprenger, 《Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models》, Front. Immunol.13, 11922, doi: 10.3389/2022.1029167.
I. Vardaxis, B. Simovski, I. Anzar, R. Stratford and T. Clancy, 《Deep learning of antibody epitopes using molecular permutation vectors》. bioRxiv, page 2024.03.20.585661, 20240321. doi: 10.1101/2024.03.20.585661.
R. Zrieq, et al. 《Predictability of COVID-19 Infections Based on Deep Learning and Historical Data》, Appl. Sci., Vol 12, Issue 16, Art. Issue 16, Jan 2022, doi: 10.3390/app12168029.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
