Applying Machine Learning to Optimize Vaccine Distribution for COVID-19

Fredrick Romanus Ishengoma


The widespread outbreak of COVID-19 has come with several challenges in terms of vaccine distribution, such as shortages in supply, logistical hurdles, and public uncertainty. However, the application of machine learning can potentially alleviate these challenges by offering valuable perspectives on the distribution of vaccines, forecasting demand, and recognizing areas with a higher transmission risk. This paper analyzes the utilization of advanced artificial intelligence techniques to optimize the allocation of vaccines for the COVID-19 virus.  This paper delves into the machine-learning approaches employed or suggested for vaccine distribution, including decision tree models, neural networks, and simulation-based methodologies. In addition, the paper addresses the challenges and limitations of using machine learning for vaccine distribution, including the necessity for high-quality data and ethical considerations. In conclusion, this paper offers a comprehensive examination of the current state of research in the application of machine learning for optimizing vaccine distribution for COVID-19 and highlights areas for further study..

Full Text:



F. Mollarasouli, N. Zare-Shehneh, and M. A. Ghaedi, "A review on corona virus disease 2019 (COVID-19): current progress, clinical features and bioanalytical diagnostic methods," Microchim Acta, vol. 189, pp. 103, 2022. doi: 10.1007/s00604-022-05167-y.

G. Farnoosh, G. Alishiri, SRH. Zijoud, et al., "Understanding the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease (COVID-19) based on available evidence - a narrative review," J. Mil. Med., vol. 22, pp. 1-11, 2020. doi: 10.30491/JMM.22.1.1.

M. Shiri and F. Ahmadizar, "An equitable and accessible vaccine supply chain network in the epidemic outbreak of COVID-19 under uncertainty," J. Ambient Intell. Human Comput., 2022. doi: 10.1007/s12652-022-03865-2.

E. A. Andoh and H. Yu, "A two-stage decision-support approach for improving sustainable last-mile cold chain logistics operations of COVID-19 vaccines," Ann. Oper. Res., 2022. doi: 10.1007/s10479-022-04906-x.

Y. Peng, E. Liu, S. Peng, et al., "Using artificial intelligence technology to fight COVID-19: a review," Artif. Intell. Rev., vol. 55, pp. 4941-4977, 2022. doi: 10.1007/s10462-021-10106-z.

D. O. Oyewola, E. G. Dada, and S. Misra, "Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic," Health Technol., vol. 12, pp. 1277-1293, 2022. doi: 10.1007/s12553-022-00712-4.

D. Qiu, Y. Yu, and L. Chen, "Emotion Analysis of COVID-19 Vaccines Based on a Fuzzy Convolutional Neural Network," Cogn. Comput., 2022. doi: 10.1007/s12559-022-10068-6.

B. Jahn, S. Friedrich, J. Behnke, et al., "On the role of data, statistics and decisions in a pandemic," AStA Adv. Stat. Anal., vol. 106, pp. 349-382, 2022. doi: 10.1007/s10182-022-00439-7.

C. Lawrence, D. J. Vick, T. Maryon, et al., "Ethical allocation of COVID-19 vaccine in the United States: an evaluation of competing frameworks for the current pandemic and future events," J. Public Health Pol., vol. 43, pp. 234-250, 2022. doi: 10.1057/s41271-022-00338-w.

A. A. Nichol and K. M. Mermin-Bunnell, "The ethics of COVID-19 vaccine distribution," J. Public Health Pol., vol. 42, pp. 514-517, 2021. doi: 10.10

Heidari, A., Jafari Navimipour, N., Unal, M., et al. "Machine learning applications for COVID-19 outbreak management." Neural Comput & Applic., vol. 34, pp. 15313-15348, 2022.

Yaesoubi, R., You, S., Xi, Q., et al. "Generating simple classification rules to predict local surges in COVID-19 hospitalizations." Health Care Manag Sci, 2023.

Aljedaani, W., Abuhaimed, I., Rustam, F., et al. "Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study." Soc. Netw. Anal. Min., vol. 12, pp. 128, 2022.

Liu, H., & Lang, B. "Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey." Applied Sciences, vol. 9, no. 20, pp. 4396, 2019. MDPI AG.

Barajas, M., Bhatkande, S., Baskaran, P., Gohel, H., & Pandey, B. "Advancing Deep Learning for Supply Chain Optimization of COVID-19 Vaccination in Rural Communities." In Proceedings of the 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, 2021, pp. 690-695.

Torku, T. K., Khaliq, A. Q. M., & Furati, K. M. "Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy." Epidemiologia, vol. 2, no. 4, pp. 564-586, 2021. MDPI AG.

AlShurman, B. A., Khan, A. F., Mac, C., Majeed, M., & Butt, Z. A. "What Demographic, Social, and Contextual Factors Influence the Intention to Use COVID-19 Vaccines: A Scoping Review." International Journal of Environmental Research and Public Health, vol. 18, no. 17, pp. 9342, 2021. MDPI AG.

Hanie, R. L., & van Rensburg, J. J. "Using Reinforcement Learning Algorithms to Explore COVID-19 Spread in South Africa." In Proceedings of the International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Durban, South Africa, 2021, pp. 1-9.

S. Khalilpourazari and H. H. Doulabi, "Using Reinforcement Learning to Forecast the Spread of COVID-19 in France," in Proceedings of the 2021 IEEE International Conference on Autonomous Systems (ICAS), Montreal, QC, Canada, pp. 1-8, 2021. doi: 10.1109/ICAS49788.2021.9551174.

S. Chen, et al., "Reinforcement Learning Based Diagnosis and Prediction for COVID-19 by Optimizing a Mixed Cost Function from CT Images," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 11, pp. 5344-5354, Nov. 2022. doi: 10.1109/JBHI.2022.3197666.

F. Trad and S. El Falou, "Towards Using Deep Reinforcement Learning for Better COVID-19 Vaccine Distribution Strategies," in Proceedings of the 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia, pp. 7-12, 2022. doi: 10.1109/CDMA54072.2022.00007.

A. Singhal, P. Singh, B. Lall, and S. D. Joshi, "Modeling and Prediction of COVID-19 Pandemic using Gaussian Mixture Model," Chaos, Solitons & Fractals, vol. 138, pp. 110023, Sep. 2020. doi: 10.1016/j.chaos.2020.110023. PMID: 32565627; PMCID: PMC7296328.

E. Külah, Y. M. Çetinkaya, A. G. Özer, and H. Alemdar, "COVID-19 Forecasting using Shifted Gaussian Mixture Model with Similarity-Based Estimation," Expert Systems with Applications, vol. 214, pp. 119034, Mar. 2023. doi: 10.1016/j.eswa.2022.119034. PMID: 36277990; PMCID: PMC9576929.

M. Hamdi, I. Hilali-Jaghdam, B. E. Elnaim, and A. A. Elhag, "Forecasting and Classification of New Cases of COVID 19 Before Vaccination using Decision Trees and Gaussian Mixture Model," Alexandria Engineering Journal, vol. 62, pp. 327-333, Jan. 2023. doi: 10.1016/j.aej.2022.07.011. PMCID: PMC9263718.

A. Marathe, A. Mandke, S. Sardeshmukh, and S. Sonawane, "Leveraging Natural Language Processing Algorithms to Understand the Impact of the COVID-19 Pandemic and Related Policies on Public Sentiment in India," in Proceedings of the 2021 International Conference on Communication Information and Computing Technology (ICCICT), Mumbai, India, pp. 1-5, 2021. doi: 10.1109/ICCICT50803.2021.9510070.

R. Tang, L. Zhang, G. Zhang, and J. Wang, "Analysis of COVID-19 Rebound Based on Natural Language Processing," in Proceedings of the 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi'an, China, pp. 333-336, 2021P.

Bose, S. Roy and P. Ghosh, "A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19 Research," in IEEE Access, vol. 9, pp. 78341-78355, 2021, doi: 10.1109/ACCESS.2021.3082108.


  • There are currently no refbacks.

Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162