ABSTRACT
The results of a preliminary analysis of the relationship between the short-term impact of air pollution exposure on hospitalizations associated with COVID-19 in Tomsk, Russia are presented. The statistical data on air pollution and COVID-19 associated hospitalization were collected and analyzed for the period from March 16, 2022 to April 14, 2022. This period corresponds to a flat plateau of confirmed COVID-19 cases after the main pandemic wave in 2022 in Tomsk and the Tomsk region which were associated with omicron strain of SARS-CoV-2. It was found that all representative peaks in a graph of daily hospitalizations coincide with the peaks in graphs of measured levels of air pollution. The increase in hospitalizations occurred on the same days when air pollution levels increased, or with a slight lag of 1-2 days. This allows us to tentatively conclude that air pollution has a quick effect on infected persons and may provoke an increase in symptoms and severity of the disease. Further detailed research is required. © 2022 SPIE.
ABSTRACT
This preliminary analysis uses a deep LSTM neural network with fastText embeddings to predict population rates of depression on Reddit in order to estimate the effect of COVID-19 on mental health. We find that year over year, depression rates on Reddit are up 50%, suggesting a 15-million person increase in the number of depressed Americans and a $7.5 billion increase in depression related spending. This finding comes at a time when uncertainty about the impact of COVID-19 on physical and economic health is still high, and suggests that in addition to those factors, mental health must be considered as well. As data becomes available, further research will be needed to validate the results of this preliminary investigation. © ACL 2020.All right reserved.
ABSTRACT
The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning. © 2022 IEEE.
ABSTRACT
In this paper, we present ArCOV-19, an Arabic COVID-19 Twitter dataset that spans one year, covering the period from 27th of January 2020 till 31st of January 2021. ArCOV-19 is the first publicly-available Arabic Twitter dataset covering COVID-19 pandemic that includes about 2.7M tweets alongside the propagation networks of the most-popular subset of them (i.e., most-retweeted and-liked). The propagation networks include both retweets and conversational threads (i.e., threads of replies). ArCOV-19 is designed to enable research under several domains including natural language processing, information retrieval, and social computing. Preliminary analysis shows that ArCOV-19 captures rising discussions associated with the first reported cases of the disease as they appeared in the Arab world. In addition to the source tweets and propagation networks, we also release the search queries and languageindependent crawler used to collect the tweets to encourage the curation of similar datasets. © WANLP 2021 - 6th Arabic Natural Language Processing Workshop
ABSTRACT
This work-in-progress practice paper reports on the experiences of using Microsoft Teams to teach a large postgraduate class on database systems during the 2020/'21 academic year, under conditions when students and lecturers were in an enforced societal lock-down because of the COVID-19 pandemic. The class was made up of 167 students of 12 different nationalities from diverse backgrounds. Determined efforts were made to create an interactive online classroom experience through the use of quizzes, practical demonstrations, worked examples, and live discussion. The chat feature of Microsoft Teams was extensively used by students to pose and answer questions, as well as to communicate with each other outside of class time. An analysis of the chat log files is presented, looking at how factors such as gender and national culture influenced behaviour, and also looking at how participation in the chat impacted upon the sense of belonging and overall performance. © Proceedings of the 2021 AIS SIGED International Conference on Information Systems Education and Research.
ABSTRACT
Due to the Coronavirus Disease (COVID-19) pandemic, the human activities in China and even in the world were reduced in 2020, which also caused the variation of the atmospheric environment, especially atmospheric aerosol emissions. In this paper, the MODIS level-3 gridded atmosphere monthly global joint product in 2019 and 2020 were collected and processed. After preliminary analysis, we found that MODIS annual aerosol optical depth (AOD) over China in 2020 is generally lower than in 2019. In some regions such as Beijing-Tianjin-Hebei and Yangtze River Delta, AOD values dropped the most in February. However, in some months and regions, AOD in 2020 is even higher than in 2019. More studies are still ongoing. © 2021 IEEE.
ABSTRACT
In order to control the spread of the pandemic of Corona-Virus Disease 2019 (COVID-19), lockdowns of various durations and intensities have been established in many countries over the world all through the year 2020. The trilateral dashboard jointly implemented by NASA, JAXA and ESA aims at exploiting remote-sensing data to evaluate the impact of these restrictions, and subsequent recovery phases on many different environmental, agriculture and economic indicators. More specifically, this paper presents the indicators implemented to monitor the impact of COVID-19 restrictions on Water Quality, together with preliminary analysis results over a few Areas of Interest. © 2021 IEEE.