Queuing Theory-Based Model for Optimization of Covid-19 Vaccination and Booster Delivery

Kanika Sharma; Shubham Agarwal; B.K. Singh

doi:10.52756/ijerr.2024.v45spl.020

Authors

Kanika Sharma Department of Mathematics, IFTM University, Moradabad-244102, Uttar Pradesh, India https://orcid.org/0009-0008-2671-5297
Shubham Agarwal Department of Mathematics, New Delhi Institute of Management, New Delhi-110062, Delhi, India https://orcid.org/0000-0001-9205-9904
B.K. Singh Department of Mathematics, IFTM University, Moradabad-244102, Uttar Pradesh, India https://orcid.org/0000-0002-3741-8270

DOI:

https://doi.org/10.52756/ijerr.2024.v45spl.020

Keywords:

Covid-19 vaccination, healthcare, optimization, queuing theory, vaccination efficiency

Abstract

Although queuing theory is commonly utilized in businesses to analyze and model processes involving waiting lines, the healthcare sector sees a difference from other industries when it comes to optimizing fixed resources under alterable demand conditions. To enhance operational effectiveness and cut down on waiting times, hospital operation managers need to be informed on the state of business processes. A scientific method to reduce systemic inefficiencies and raise patient satisfaction is the queuing theory. The objective of this study is to use queuing theory to optimize COVID-19 vaccination and booster delivery. This study discussed two distinct models, one for bigger MV hubs and the other for smaller GP vaccination clinics. The current study demonstrated how these models may be used to anticipate staffing needs to prevent bottlenecks, predict daily throughput given staff capacity limits, and simulate the queuing process. With respectable face validity, we produced accurate estimates of the distributions of given service times and overall processing times. In the future, this may be improved by carrying out a time-use survey to get empirical data on total processing time, which could be compared to the projected processing time of the model and service times for each station, which would help guide the model's inputs.

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