ABSTRACT
The development of the vaccine for the control of COVID-19 is the need of hour. The immunity against coronavirus highly depends upon the vaccine distribution. Unfortunately, vaccine hesitancy seems to be another big challenge worldwide. Therefore, it is necessary to analysis and figure out the public opinion about COVID-19 vaccines. In this era of social media, people use such platforms and post about their opinion, reviews etc. In this research, we proposed BERT+NBSVM model for the sentimental analysis of COVID-19 vaccines tweets. The polarity of the tweets was found using TextBlob(). The proposed BERT+NBSVM outperformed other models and achieved 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. Thus, these sentimental and spatial analysis helps in world-wide pandemics by identify the people's attitudes towards the vaccines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the trust value of the function point method and the value of the Markov Chain. © 2023 - IOS Press. All rights reserved.
ABSTRACT
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.
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We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the "demand-driven” properties of the SP and DRO solutions. Our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage. © 2022 Elsevier Ltd
ABSTRACT
Vaccination could be a critical preventative strategy against coronavirus disease 2019 (COVID-19), and it is essential to understand the vaccine's usability in the general population. A safe and effective vaccination is the most effective way to terminate this epidemic. Many communities throughout the globe have expressed concerns regarding the efficacy and side effects of coronavirus SARS CoV2 vaccinations. Vaccines are now being rushed to market. Many papers have been published on COVID-19 vaccine, hesitancy, acceptance rate, local survey, vaccine distribution, vaccine information, etc. However, none of them mentioned any potential side effects from the COVID-19 vaccination for those with pre-existing disease like Asthma. The study aimed to describe the possible side effects after getting COVID-19 vaccines (Moderna, Pfizer and Janssen) for those who have a pre-existing disease like Asthma. © 2022 ACM.
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The aim is to construct a country-dimension knowledge graph of COVID-19 vaccines from the information of COVID-19 vaccines and to analyze the leading countries of vaccine R&D by combining the advantages of easy operation and intuitive feeling of knowledge graph visualization, to provide a reference for Chinese vaccine R&D departments and international cooperation. In this paper, through data collection, based on entity extraction and relationship construction, a knowledge graph of country dimensions was established by specifying the central vaccine R&D countries and vaccine distribution, and multidimensional microdata such as word frequency and betweenness centrality were combined to analyze the national characteristics of the COVID-19 vaccine. The analysis of the knowledge graph of the country dimension of the COVID-19 vaccine shows that countries with robust technology and economies, such as the US and China, choose to develop vaccine distribution independently, countries with advanced economies, such as Saudi Arabia, decide to purchase vaccine distribution, and less developed countries, such as South Africa and Latin America, need international aid for vaccines or purchase low-cost vaccines. This paper constructs the correlation between nodes and nodes of the COVID-19 vaccine with the help of a knowledge graph, systematically and comprehensively reveals the research mainstay and distribution model of the COVID-19 vaccine from the national level, and provides rationalized suggestions for international cooperation in vaccine R&D in China. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
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.
ABSTRACT
The COVID-19 epidemic that emerged in China two years ago has threatened the world due to its rapid contagion, and it was examined by many academics from several aspects, one of which is the field of medicine. The problem of distributing the developed vaccines to hospitals and vaccination centers in a fast and reliable manner has arisen. To cope with this epidemic, the problem of vaccine distribution has become an important issue. Distributing the vaccines from one single point to all provincial centers will cause a delay in the transfer process. Considering mRNA vaccines are of a special type of vaccine that needs to be stored below - 70, establishment of cold storages in every hospital at such a low temperature will be costly. Therefore, it is necessary to determine distribution centers where these cold storages can be installed. Moreover, to have a fast and economical way to distribute the vaccines, additional distribution centers should be established considering the insufficiency of the current facilities due to increased demand. To determine the location of the vaccine distribution centers, the distance between the vaccination centers and the vaccine distribution centers should be taken into account as well as vaccine demand of hospitals. Looking at the previous studies, it can be seen that many scholars used the p-median model to determine the facility location, however, there has not been any study on the selection of the vaccine distribution facility location using the p-median model. In this study, a p-median model is developed to select the location of new vaccine distribution centers to be opened. In this model, different alternatives were discussed in order to determine the new distribution centers needed in Ankara due to the increased vaccination rates. The model is solved with the GAMS 24.1.3 program. As a result, the hospital where the best solution was obtained, has been determined as the new distribution center. In the last part of the study, the advantages of opening a new vaccine distribution center are mentioned. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Optimum location of vaccine distribution and Emergency Operation Centers (EOCs) is imperative to ensuring prompt and efficient vaccination of eligible population in any location of interest. The proximity of these vaccination centers is likely to positively affect the decision of the target population to present themselves for vaccination. In this paper, a computational model for optimizing the number and determining the location of depots or vaccine distribution centers, and amounts of vaccines to be stocked at each center, to satisfy the needs of the local population is proposed. A modified K-means++ is used to optimize the number of required centers and the approximate locations to ensure the usage of the least possible cost. The algorithm allows planners to enter two initial specific locations as depots, thereby avoiding the usual random selection of initial points. Using geospatial and population data, the resulting clusters are divided into two, on each iteration. Heap sort is used to select the next centroid. Optimization of these locations is iteratively done, until there are no more changes. An optimized number of vaccine distribution centers for any region of interest can be obtained. It ensures that least possible cost is used. Our algorithm avoids the usual random outcomes associated with K-means and provides a more efficient clustering output, with an improved time complexity. The application of the proposed algorithm to a real-world test instance indicates its effectiveness. © 2022 IEEE.
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In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved
ABSTRACT
Ever since the onset of COVID-19, the healthcare fraternity and supply chains have faced severe disruptions like never before in the near past. The higher transmission rate of the virus has spread it worldwide. Vaccination is an inevitable phase to curtail the spread of the pandemic. The present study aims to develop an agent-based model to explain the distribution network of the vaccine. Grey relational analysis has been carried out to rank different states of India based on the critical variables that govern the transmission of the virus. This would help the policymakers rationally distribute the vaccines across different places. Further, sensitivity analysis has been performed with ten scenarios to compare the effects of positive and negative events, word of mouth, and the number of sessions on the distribution of vaccines. Increasing the sessions conducted per day from 163.33 by 40 and 80 increased the proportion vaccinated by 38.32 and 73.89 percentages, respectively. Copyright © 2021 Inderscience Enterprises Ltd.
ABSTRACT
In this digital era, machine learning (ML) is becoming more common in the healthcare industry. It plays many essential roles in the medical field including clinical forecasting, visualization, and even automated diagnostics. This paper focuses on the future prediction of COVID-19 vaccination rates in different countries. Considering how destructive the novel Coronavirus has been and its continuous mutation and spread, clinical interventions such as vaccines serve as a ray of hope for many individuals. As of 2021, an estimated total of 8,687,201,202 vaccine doses by numerous biopharmaceutical manufacturers have been administered worldwide [1]. This study intends to estimate the probable increase or decrease in global vaccination rates, as well as analyze the correlation between future trends of daily vaccinations and new COVID-19 cases, along with deaths and reproduction rates. Three models were utilized in forecasting and comparing the overall prediction toward the COVID19 vaccine rates;Auto-Regressive Integrated Moving Average (ARIMA), an ML approach, Long-Short Term Memory (LSTM), an artificial Recurrent Neural Networks (RNN), and Prophet which is based on an additive model. The Vector Autoregression (VAR) model will also be utilized to compare COVID-19 cases, deaths and reproduction rates to that of COVID-19 vaccine growth. ARIMA resulted to be the best model, while Prophet turned out to be the worst-performing model. In general, our comparison of employing the ARIMA model vs the other three results in the conclusion that adopting this method shows to be a more effective approach in projecting vaccination growth in the future. Furthermore, a visible increase in future daily vaccinations can be seen to be correlated with the increase in COVID-19 cases, deaths reproduction rates, and a fluctuating trend in COVID-19 deaths. © 2022 IEEE.
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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.
ABSTRACT
With the continuous rise of the COVID-19 cases worldwide, it is imperative to ensure that all those vulnerable countries lacking vaccine resources can receive sufficient support to contain the risks. COVAX is such an initiative operated by the WHO to supply vaccines to the most needed countries. One critical problem faced by the COVAX is how to distribute the limited amount of vaccines to these countries in the most efficient and equitable manner. This paper aims to address this challenge by first proposing a data-driven risk assessment and prediction model and then developing a decision-making framework to support the strategic vaccine distribution. The machine learning-based risk prediction model characterizes how the risk is influenced by the underlying essential factors, e.g., the vaccination level among the population in each COVAX country. This predictive model is then leveraged to design the optimal vaccine distribution strategy that simultaneously minimizes the resulting risks while maximizing the vaccination coverage in these countries targeted by COVAX. Finally, we corroborate the proposed framework using case studies with real-world data. © 2022 IEEE.
ABSTRACT
The rapid development of artificial intelligence techniques is significantly promoting the resolution of various important decision-making issues such as material distribution, generation line optimization scheduling, and path planning. Currently, SARS-CoV-2 is raging over the world, and it is valuable to propose a vaccine distribution strategy to utilize limited vaccine resources rationally. In this paper, we aim to propose an optimal vaccine distribution strategy based on deep reinforcement learning(DRL) approaches. An End-to-End vaccine distribution model is proposed by combining the Deep Reinforcement Learning model and LinUCB algorithm to get an optimistic strategy of allocation. Experiment results demonstrated that vaccine distribution strategies based on this model show a strong capacity to control the epidemic and ensure stable government revenue compared with baseline strategies. © 2021 IEEE.
ABSTRACT
As COVID-19 becoming a global epidemic, owing to the interventions' operation limited efficacy and virus' super transmission ability, the vaccine is considered the most potent method left to cease the COVID-19 effectively. At the beginning of the vaccine distribution policy design, there were many real concerns: vaccine priority, budget control, vaccine inventory limitation, and expected objectives making the problem complex. The research optimised the vaccine distribution policy (VDP) in an explicit form incorporated in an age-stratified SEIR model based on the proposed policy optimisation methodology. The VDP could explain when and how many vaccines to take for each age group. The designed evaluation system consisted of direct policy cost, indirect healthcare cost, and extra financial budget during the pandemic, combined as a weighted sum equalling one to suit flexible scenarios and decision-makers' requirements. A case study with ground truth data in the U.K was implemented, where the optimised VDP could decrease the comprehensive cost and suppress the virus transmission. Furthermore, the sensitivity analysis demonstrated the effect of some critical parameters for optimised VDP. The vaccination priority and policy objectives' weight combination play a significant role in impacting the VDP optimisation. The research could be a framework for flexible vaccination policy design in different scenarios by changing weights, vaccine limitations, and other initial parameter configurations. © 2021 ACM.
ABSTRACT
Vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel coronavirus pneumonia (COVID-19) pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely-scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can pre-distribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and hence accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts, and hence contribute to accelerating the achievement of herd immunity. IEEE
ABSTRACT
Over 170 nations have been affected from Coronavirus disease 2019(COVID-19). In nearly all the afflicted countries, the number of afflicted people and dying people has been rising at a frightening rate. Our biggest option for halting the pandemic's spread is a COVID-19 vaccination. But vaccines are an exhaustible resource. Accurate prediction of vaccine distribution by already implemented policies is critical to assisting policymakers in making sufficient decisions in containing COVID-19 pandemic. Forecasting approaches can be utilized, aiding in the development of better plans and the making of sound judgments. These approaches analyze past events to make more accurate predictions about what will happen in the future according to the current implemented strategy. The effectiveness of various LSTM (Long Short-Term Memory) models as well as the ARIMA (Auto-Regressive Integrated Moving Average) model in projecting vaccine distribution for COVID-19 patients. © 2022 IEEE.
ABSTRACT
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.
ABSTRACT
Vaccination of the global population against COVID-19 is one of the challenging tasks in supply chain management that humanity has ever faced. The rapid roll-out of the COVID-19 vaccine is a must for making the worldwide immunization campaign successful, but its effectiveness depends on the availability of an operational and transparent distribution chain that can be audited by all related stakeholders. In this paper, the necessity of Blockchain and Machine Learning in supply-chain management with demand forecasting of the COVID-19 vaccine has been presented. The aim is to understand how the convergence of Blockchain technology and ML monitor the prerequisite of vaccine distribution with demand forecasting. Here, we have proposed an approach consists of Blockchain and Machine Learning which will be used to ensure the seamless COVID-19 vaccine distribution with transparency, data integrity, and end-to-end traceability for reducing risk, assuring the safety, and also immutability. Besides this, we have performed demand forecasting for appropriate COVID-19 vaccines according to the geographical area and the storage facilities. Lastly, we have discussed research challenges and also mentioning the limitations with future directions. © 2021 IEEE.