Even though vaccination programs have now started in earnest across the globe and in Qatar, vaccine hesitancy remains a barrier to effectively tackling the pandemic. Many factors influence willingness to take vaccines including safety, efficacy, and side effects. Given their proximity to research and education, university students and employees represent an interesting cohort in which to investigate vaccine hesitancy. The aim of this study was to assess the attitudes of Qatar University employees and students towards the COVID-19 vaccine. In total, 231 employees and 231 students participated in an online cross-sectional study in February 2021. Of the sample, 62.6% were willing to take a vaccine against COVID-19. Participants with or taking postgraduate degrees were more willing to take the vaccine compared to participants with or taking a diploma or bachelor’s degree (p < 0.001). Males had a higher rate of vaccine acceptance (p < 0.001). In the group that regarded flu vaccination as important, 13% were unwilling to take COVID-19 vaccine. There were no associations between willingness to vaccinate and vaccine/virus knowledge and social media use. Participants showed a high level of concern regarding vaccine side effects in themselves or their children. Two-thirds agreed or strongly agreed that they would take the vaccine if it was mandatory for international travel. Our participants were neutral to the origin of vaccine development. These findings, which represent data collected after the start of the national vaccination program, show that vaccine hesitancy persists in the Qatari population and that some groups, such as undergraduate students, could benefit from specific, targeted public health campaigns.

In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.

In recent years, new applications emerged that produce data streams, such as stock data and sensor networks. Therefore, finding frequent subsequences, or clusters of subsequences, in data streams is an essential task in data mining. Data streams are continuous in nature, unbounded in size and have a high arrival rate. Due to these characteristics, traditional clustering algorithms fail to effectively find clusters in data streams. Thus, an efficient incremental algorithm is proposed to find frequent subsequences in multiple data streams. The described approach for finding frequent subsequences is by clustering subsequences of a data stream. The proposed algorithm uses a window model to buffer the continuous data streams. Further, it does not recompute the clustering results for the whole data stream at every window, but rather it builds on clustering results of previous windows. The proposed approach also employs a decay value for each discovered cluster to determine when to remove old clusters and retain recent ones. In addition, the proposed algorithm is efficient as it scans the data streams once and it is considered an Any-time algorithm since the frequent subsequences are ready at the end of every window.

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