Vaccine Supply Forecasting and Optimization using Deterministic and Probabilistic Approaches

When the pandemic was at its peak, it was a quite difficult task for the government to schedule vaccine supply in various districts of a state. This task became further difficult when vaccines were required to be supplied to various Covid Vaccination Centers (CVCs) at a granular level. This is because there was no data regarding the trend being acquired at each CVC and the population distribution is non-uniform across the district. This led to the arousal of an ambiguous situation for a certain period and hence mismanagement. Now that we have sufficient data across each CVC, we can work on a time series analysis of vaccine requirements in which we can essentially forecast the number of administered doses and optimize the wastage at all atomic CVC levels. © 2023 IEEE.

Supply Chain Network Model using Multi-Agent Reinforcement Learning for COVID-19

The COVID-19 vaccination management in Japan has revealed many problems. The number of vaccines available was clearly less than the number of people who wanted to be vaccinated. Initially, the system was managed by making reservations with age group utilizing vaccination coupons. After the second round of vaccinations, only appointments for vaccination dates were coordinated and vaccination sites were set up in Shibuya Ward where the vaccine could be taken freely. Under a shortage of vaccine supply, the inability to make appointments arose from a failure to properly estimate demand. In addition, the vaccine expired due to inadequate inventory management, resulting in the vaccine being discarded. This is considered to be a supply chain problem in which appropriate supply could not be provided in response to demand. In response to this problem, this paper examines whether it is possible to avoid shortage and stock discards by a decentralized management system for easy on-site inventory control instead of a centralized management system in real world. Based on a multi-agent model, a model was created to redistribute inventory to clients by predicting future shortage based on demand fluctuations and past inventory levels. The model was constructed by adopting the Kanto region. The validation results of the model showed that the number of discards was reduced by about 70% and out-of-stocks by about 12% as a result of learning the dispersion management and out-of-stock forecasting © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

Analysis of Strategies to Tackle the Environmental Impact of the Vaccine Supply Chain: A Fuzzy DEMATEL Approach

To combat the COVID-19 pandemic, the scientific community has progressed from discovering antivirals to the large-scale production of vaccines. Mass vaccination programs to curb the COVID-19 pandemic started in many parts of the world at the beginning of 2021. Mass vaccination aims to exit from health emergencies by vaccinating all the population with the required dose in the shortest possible time. The production rate has been boosted, and many new production facilities have been opened to fulfill worldwide demand. The objective of the vaccination program is to maximize the medical benefit with the lowest cost and equitable distribution of vaccines worldwide. However, the environmental impact of this long-run immunization program has received very little attention. This study explores the environmental impact of the vaccine supply chain (VSC) and analyzes the mitigation strategies to minimize it without affecting the medical, economic, and social benefits of vaccination. The fuzzy DEMATEL technique has been used to prioritize the mitigation techniques and find cause and effect relations among them. The finding of studies shows that the "optimal vial design” is most important, and "vaccine awareness and education” is the most impactful strategy to tackle the environmental impact of VSC. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Blockchain technology and vaccine supply chain: Exploration and analysis of the adoption barriers in the Indian context

The vaccine distribution system, being a bio-pharmaceutical cold chain, is a complicated and sensitive system that must be effectively managed and maintained due to its direct impact on public health. However, vaccine supply chains continue to be affected by concerns, including vaccine expiry, inclusion of counterfeit vaccines, and vaccine record fraud. The blockchain technology integrated with the Internet of Things (IoT) can create a solution for global vaccine distributions with improved trust, transparency, traceability, and data management, which will help monitor the cold chain, tackle counterfeit drugs, surveillance, and waste management. Several theoretical models for vaccine management with blockchain have recently been published, and a few pilot studies for COVID-19 vaccine management using blockchain have been started in India. Still, full-scale adoption of blockchain technology in vaccine distribution and management has yet to be achieved due to underlying barriers. This study explores the adoption barriers utilizing Technology-Organization-Environment (TOE) framework with the help of extant literature and inputs from administrators, academics, immunization, and blockchain experts and then analyzed using the Delphi and fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The finding shows that the requirement of change in organizational structure and policies is the most prominent barrier, and the barrier related to requirement of large-scale IoT infrastructure and lack of technical expertise are the most impactful barriers. The theoretical contribution of this study lies in the identification and analysis of barriers that should be addressed to achieve blockchain technology adoption in the vaccine supply chain. © 2022 Elsevier B.V.

Vaccine Supply Optimization and Forecasting using Random Forest and ARIMA Models

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.

An Exploratory Analysis of the US COVID-19 Vaccine Distribution

The pandemic of Covid-19 was a huge challenge for people, the economies of companies and nations. The supply chain, which is a link from suppliers to customers, is one of the many sectors hugely affected by the Covid-19 pandemic. Many suppliers were forced to shut down operations due to the pandemic and the transportation industry suffered immensely. World leaders, medical practitioners and pharmaceutical companies began to talk about vaccine development to help in the fight against the pandemic. A breakthrough in the Covid-19 vaccine development brought smiles again to the world as many countries were already struggling to deal with the effect of this pandemic. Since the supply chain industry has been gravely impacted by the pandemic, there rises another challenge in the distribution of the developed Covid-19 Vaccine. Using Statistical Analysis System (SAS) and a combination of different multivariate methods, this research explores the United States Covid-19 and Vaccine distribution dataset to uncover trends affecting the Covid-19 Vaccine Supply Chain (VSC). Furthermore, this research provides some suggestions on how to improve the Covid-19 VSC using supply chain drivers such as facilities, transportations, and information. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

A Decision Support System to facilitate Vaccination for Covid-19 pandemic

Global distribution of COVID-19 vaccines is one of the world's most challenging logistics tasks. This study proposes a decision support system that integrates semaphores to facilitate distribution and vaccination process of COVID-19 vaccines. Two vaccine supplies namely Covishield (CTRI/2020/08/027170) and Covaxin (CTRI/2020/11/028976), were formulated to operationalise a two-dose vaccination program in India. In comparison with other vaccine distribution plans being executed without any prioritisation, such as on a random basis, the plans generated by the proposed decision support system ensure prioritised vaccination for the vulnerable population. Additional approach is taken to arrange the supply of vaccines using counting semaphores which eliminates the problem of people having to wait at vaccination centres and also ensuring the priority of people coming for second dose with additional consideration to aged people. © 2022 IEEE.

A Bibliometric Analysis of the Logistical Challenges and Methods for Vaccine Distribution Under the Pandemic

With the emergence and the widespread of the COVID-19 pandemic across the globe, there is an urgent need to effectively control the disease spread through mass vaccination. Several COVID-19 vaccines, e.g., Pfizer/BioNtech and Moderna, etc., have been proven highly effective and have been distributed and administrated in many countries. These vaccines need to be produced in large quantities and transported through dedicated cold chain logistic networks to maintain the quality. Currently, the major logistical challenges are associated with the effective distribution of COVID-19 vaccines to hospitals and healthcare centers in different countries. To better understand and tackle these challenges, we conduct a bibliometric analysis on vaccine supply chains and cold chain logistics for vaccine distribution. The current research landscape is investigated through four main classification analyses including journal co-citation analysis, keyword co-occurrence analysis, country collaborations analysis, and document co-citation analysis. These analyses allow us to identify the publication trends, the most popular journals in this field, the collaborations between countries and to identify the key areas where most attention is given. Finally, the methods are summarized, and the future research opportunities for effective COVID-19 vaccine distribution are identified. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Understanding the Issues Surrounding COVID-19 Vaccine Roll Out via User Tweets

Vaccinations have emerged as one of the key tools to combat the COVID-19 pandemic, reduce infections and to enable safe re-opening of societies. Vaccinating the entire world population is a challenging undertaking and with demand far exceeding supply in the world, it is expected that topics surrounding vaccinations generate a wide array of discussions. Therefore, in this paper, we collect data from Twitter during the early days of the COVID-19 vaccination program and adopt a linguistic approach to better understand and appreciate peoples’ concerns and opinions with regards to the roll out of the vaccines. We begin by studying the term frequencies (i.e., unigrams and bigrams) and observe discussions around vaccination doses, receiving doses, vaccine supply, scheduling appointments and wearing masks as the vaccination efforts get underway. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in the tweets and the main issues being discussed in each topic. We observe that our dataset has nine distinct topics. For example, we observe topics related to vaccine distribution, eligibility, scheduling and COVID variants. We then study the sentiment of the tweets with respect to each of the nine topics and observe that the overall sentiment is negative for most of the topics. We only observe a higher percentage of positive sentiment for topics related to obtaining information and schools. Our research lays the foundation to conduct a more fine-grained analysis of the various issues faced by the people as the pandemic recedes over the course of the next few years. © 2021, Springer Nature Switzerland AG.