Applying Machine Learning to Optimize Vaccine Distribution for COVID-19

Fredrick Romanus Ishengoma

Abstract


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..


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References


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