Unveiling Vaccine Hesitancy on Twitter: Analyzing Trends and Reasons during the Emergence of COVID-19 Delta and Omicron Variants.

Given the high amount of information available on social media, the paper explores the degree of vaccine hesitancy expressed in English tweets posted worldwide during two different one-month periods of time following the announcement regarding the discovery of new and highly contagious variants of COVID-19-Delta and Omicron. A total of 5,305,802 COVID-19 vaccine-related tweets have been extracted and analyzed using a transformer-based language model in order to detect tweets expressing vaccine hesitancy. The reasons behind vaccine hesitancy have been analyzed using a Latent Dirichlet Allocation approach. A comparison in terms of number of tweets and discussion topics is provided between the considered periods with the purpose of observing the differences both in quantity of tweets and the discussed discussion topics. Based on the extracted data, an increase in the proportion of hesitant tweets has been observed, from 4.31% during the period in which the Delta variant occurred to 11.22% in the Omicron case, accompanied by a diminishing in the number of reasons for not taking the vaccine, which calls into question the efficiency of the vaccination information campaigns. Considering the proposed approach, proper real-time monitoring can be conducted to better observe the evolution of the hesitant tweets and the COVID-19 vaccine hesitation reasons, allowing the decision-makers to conduct more appropriate information campaigns that better address the COVID-19 vaccine hesitancy.

Health risks of airplane boarding methods with apron buses when some passengers disregard safe social distancing.

Many airlines instituted social distancing practices to keep passengers safe during the pandemic. The practices include keeping the middle seats empty, reducing the number of passengers taking an apron bus from the terminal to the airplane, and prescribing that passengers maintain 1 m social distance of separation from other passengers in the aisle while advancing to their seats. However, not all passengers comply with a prescribed 1 m aisle social distance. Through agent-based simulations of passenger boarding when apron buses are used, we examine boarding policies adapted for the pandemic when the level of passenger compliance varies. To compare policies, we consider the duration of time that passengers are too close to other passengers while walking or standing in the aisle. We consider other health metrics from previous research and the time to complete boarding of the airplane. We find that the WilMA-Spread and Reverse-pyramid-Spread boarding methods provide favorable outcomes. Airlines should use WilMA-Spread if their primary concern is the risk to passengers while walking down the aisle and Reverse-pyramid-Spread if they want faster times to complete boarding of the airplane and reduced risk to aisle seat passengers from later boarding passengers. The level of the passengers' non-compliance with the prescribed aisle social distance can impact a health metric by up to 6.75%-depending on the boarding method and metric. However, non-compliance reduces the time to complete boarding of the airplane by up to 38.8% even though it increases the average time an individual passenger spends boarding.

New Wave of COVID-19 Vaccine Opinions in the Month the 3rd Booster Dose Arrived.

Vaccination has been proposed as one of the most effective methods to combat the COVID-19 pandemic. Since the day the first vaccine, with an efficiency of more than 90%, was announced, the entire vaccination process and its possible consequences in large populations have generated a series of discussions on social media. Whereas the opinions triggered by the administration of the initial COVID-19 vaccine doses have been discussed in depth in the scientific literature, the approval of the so-called 3rd booster dose has only been analyzed in country-specific studies, primarily using questionnaires. In this context, the present paper conducts a stance analysis using a transformer-based deep learning model on a dataset containing 3,841,594 tweets in English collected between 12 July 2021 and 11 August 2021 (the month in which the 3rd dose arrived) and compares the opinions (in favor, neutral and against) with the ones extracted at the beginning of the vaccination process. In terms of COVID-19 vaccination hesitance, an analysis based on hashtags, n-grams and latent Dirichlet allocation is performed that highlights the main reasons behind the reluctance to vaccinate. The proposed approach can be useful in the context of the campaigns related to COVID-19 vaccination as it provides insights related to the public opinion and can be useful in creating communication messages to support the vaccination campaign.

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement.

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.

COVID-19 Vaccine Hesitancy in the Month Following the Start of the Vaccination Process.

The occurrence of the novel coronavirus has changed a series of aspects related to people's everyday life, the negative effects being felt all around the world. In this context, the production of a vaccine in a short period of time has been of great importance. On the other hand, obtaining a vaccine in such a short time has increased vaccine hesitancy and has activated anti-vaccination speeches. In this context, the aim of the paper is to analyze the dynamics of public opinion on Twitter in the first month after the start of the vaccination process in the UK, with a focus on COVID-19 vaccine hesitancy messages. For this purpose, a dataset containing 5,030,866 tweets in English was collected from Twitter between 8 December 2020-7 January 2021. A stance analysis was conducted after comparing several classical machine learning and deep learning algorithms. The tweets associated to COVID-19 vaccination hesitancy were examined in connection with the major events in the analyzed period, while the main discussion topics were determined using hashtags, n-grams and latent Dirichlet allocation. The results of the study can help the interested parties better address the COVID-19 vaccine hesitancy concerns.

Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets.

This paper studies the problem of tangible assets acquisition within the company by proposing a new hybrid model that uses linear programming and fuzzy numbers. Regarding linear programming, two methods were implemented in the model, namely: the graphical method and the primal simplex algorithm. This hybrid model is proposed for solving investment decision problems, based on decision variables, objective function coefficients, and a matrix of constraints, all of them presented in the form of triangular fuzzy numbers. Solving the primal simplex algorithm using fuzzy numbers and coefficients, allowed the results of the linear programming problem to also be in the form of fuzzy variables. The fuzzy variables compared to the crisp variables allow the determination of optimal intervals for which the objective function has values depending on the fuzzy variables. The major advantage of this model is that the results are presented as value ranges that intervene in the decision-making process. Thus, the company's decision makers can select any of the result values as they satisfy two basic requirements namely: minimizing/maximizing the objective function and satisfying the basic requirements regarding the constraints resulting from the company's activity. The paper is accompanied by a practical example.

Airplane Boarding Methods that Reduce Risk from COVID-19.

Airlines have recently instituted practices to reduce the risk of their passengers becoming infected with the novel coronavirus (SARS-CoV-2). Some airlines block their airplanes' middle seats to preserve social distancing among seated passengers. In this context, we present six new boarding methods and compare their performance with that of the two best boarding methods used to date with social distancing. We evaluate the eight boarding methods using three performance metrics related to passenger health and one operational metric (airplane boarding time) for a one-door airplane. The three health metrics reflect the risks of virus spread by passengers through the air and surfaces (e.g. headrests and seat arms) and consider the amount of aisle social distancing between adjacent boarding passengers walking towards their seats. For an airline that highly values the avoidance of window seat risk, the best method to use is one of the new methods: back-to-front by row - WilMA, though it will result in a longer time to complete boarding of the airplane. Airlines placing greater emphasis on fast boarding times- while still providing favorable values for the health metrics-will be best served by using new methods back-to-front by row - WilMA - offset 2 and - offset 3 when aisle social distancing is 1 m and 2 m respectively.

Adapting the reverse pyramid airplane boarding method for social distancing in times of COVID-19.

Social distancing resulting from the new coronavirus (SARS-CoV2) has disrupted the airplane boarding process. Social distancing norms reduce airplane capacity by keeping the middle seats unoccupied, while an imposed aisle social distance between boarding passengers slows the boarding. Recent literature suggests the Reverse Pyramid boarding method is a promising way to reduce health risk and keep boarding times low when 10 apron buses (essentially 10 boarding groups) are used to transport passengers from the airport terminal to a two-door airplane. We adapt the Reverse Pyramid method for social distancing when an airplane is boarded using a jet bridge that connects the terminal the airplane's front door. We vary the number of boarding groups from two to six and use stochastic simulation and agent-based modelling to show the resulting impact on four performance evaluation metrics. Increasing the number of boarding groups from two to six reduces boarding time only up to four groups but continues to reduce infection risk up to six groups. If the passengers carry fewer luggage aboard the airplane, health risks (as well as boarding times) decrease. One adaptation of the Reverse Pyramid (RP) method (RP-Spread) provides slightly faster boarding times than the other (RP-Steep), when luggage volumes are high, while RP-Steep results in less risk to window seat passengers from later-boarding passengers walking by their row. Increasing the minimum aisle social distance from 1 m to 2 m increases boarding times but results in lower health risks to passengers walking down the aisle and to the previously seated passengers they pass.

Social distancing in airplane seat assignments.

This paper addresses the airplane passengers' seat assignment problem while practicing social distancing among passengers. We proposed a mixed integer programming model to assign passengers to seats on an airplane in a manner that will respect two types of social distancing. One type of social distancing refers to passengers being seated far enough away from each other. The metric for this type of social distancing is how many passengers are seated so close to each other as to increase the risk of infection. The other type of social distancing refers to the distance between seat assignments and the aisle. That distance influences the health risk involved in passengers and crew members walking down the aisle. Corresponding metrics for both health risks are included in the objective function. To conduct simulation experiments, we define different scenarios distinguishing between the relative level of significance of each type of social distancing. The results suggest the seating assignments that best serve the intention of the scenarios. We also reformulate the initial model to determine seat assignments that maximize the number of passengers boarding an airplane while practicing social distancing among passengers. In the last part of this study, we compare the proposed scenarios with the recommended middle-seat blocking policy presently used by some airlines to keep social distancing among passengers. The results show that the proposed scenarios can provide social distancing among seated passengers similar to the middle-seat blocking policy, while reducing the number of passengers seated close to the aisle of an airplane.

Evaluation of Boarding Methods Adapted for Social Distancing When Using Apron Buses.

Social distancing reduces the risk of people becoming infected with the novel coronavirus (SARS-CoV-2). When passengers are transported from an airport terminal to an airplane using apron buses, safe social distancing during pandemic times reduces the capacity of the apron buses and has led to the practice of airlines keeping the middle seats of the airplanes unoccupied. This article adapts classical boarding methods so that they may be used with social distancing and apron buses. We conduct stochastic simulation experiments to assess nine adaptations of boarding methods according to four performance metrics. Three of the metrics are related to the risk of the virus spreading to passengers during boarding. The fourth metric is the time to complete boarding of the two-door airplane when apron bus transport passengers to the airplane. Our experiments assume that passengers advancing to their airplane seats are separated by an aisle social distance of 1 m or 2 m. Numerical results indicate that the three variations (adaptations) of the Reverse pyramid method are the best candidates for airlines to consider in this socially distanced context. The particular adaptation to use depends on an airline's relative preference for having short boarding times versus a reduced risk of later boarding passengers passing (and thereby possibly infecting) previously seated window seat passengers. If an airline considers the latter risk to be unimportant, then the Reverse pyramid - Spread method would be the best choice because it provides the fastest time to board the airplane and is tied for the best values for the other two health risk measures.