Vaccine Supply Optimization and Forecasting using Random Forest and ARIMA Models
3rd IEEE Global Conference for Advancement in Technology, GCAT 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2191789
ABSTRACT
In order to tackle the Corona Virus Disease, it took a considerable amount of time for the governments to come up with effective and efficient vaccines. After the vaccines were developed, the next challenge was to supply the vaccines to various designated centers based on demographics, population distribution, and other factors. The whole system for vaccine supply played a vital role during the COVID-19 pandemic. We also saw a lot of haphazard and mismanagement in some places especially when the cases per day surged high, as people weren't prepared for such a situation. Now that we have got enough data, we can use it to optimize the vaccine supply across various Covid Vaccination Centers and be prepared for any such circumstances in the future. In this paper, we have proposed a two-step approach where considering the past supply and wastage data we performed a classification task that indicates whether doses are to get wasted at a given center. If yes, we then perform demand forecasting based on the number of administered doses so that the wastage can be reduced, and supply can be optimized. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Randomized controlled trials
Topics:
Vaccines
Language:
English
Journal:
3rd IEEE Global Conference for Advancement in Technology, GCAT 2022
Year:
2022
Document Type:
Article
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