ABSTRACT
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.
ABSTRACT
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.
