Factors related to COVID-19 Incidence and Mortality rate in Gyeongsangbuk-do, Korea (경상북도 지역의 코로나19 발생률 및 사망률 관련요인)
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- Journal of agricultural medicine and community health
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- v.45 no.4
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- pp.235-244
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- 2020
Objectives: Gyeongsangbuk-do has entered a super-aged society with 20.7% of the population aged 65 and older. As of April 30, 2020, the death rate of COVID-19(3.8 people) in Gyeongsangbuk-do is higher than the national mortality rate (2.3 people), and the fatality rate of COVID-19 by age accounts for more than half of the total of 58.6%, so it is time to propose to prevent infectious diseases in the event of additional infectious disease disasters COVID-19. Methods: We collected daily data on the number of confirmed cases and deaths due to COVID-19 from 19 February to 30 April 2020. The data collected was evaluated using the SPSS 21.0 statistical package. Results: As a result of comparing the incidence and death-related factors of confirmed patients in Gyeongsangbuk-do, there were significant differences in age group (p<0.001), underlying disease (p<0.001), and residence type (p<0.033). Conclusion: Factors affecting the mortality rate of confirmed patients in Gyeongsangbuk-do have been combined with individual level factors(age, gender, underlying disease), which means individual characteristics that have existed since before the disease, and regional level factors(Type of Residence), which are external factors that enable the use of medical resources. Therefore, each local government is required to establish preventive measures considering individual and regional level factors.
An electric scooter(e-scooter), one popularized micro-mobility vehicle has shown rapidly increasing use in many cities. In South Korea, the use of e-scooters has greatly increased, as some companies have launched e-scooter sharing services in a few large cities, starting with Seoul in 2018. However, the use of e-scooters is still controversial because of issues such as parking and safety. Since the perception toward the means of transportation affects the mode choice, it is necessary to track the trends for electric scooters to make the use of e-scooters more active. Hence, this study aimed to analyze the trends related to e-scooters. For this purpose, we analyzed news articles related to e-scooters published from 2014 to 2020 using dynamic topic modeling to extract issues and sentiment analysis to investigate how the degree of positive and negative opinions in news articles had changed. As a result of topic modeling, it was possible to extract three different topics related to micro-mobility technologies, shared e-scooter services, and regulations for micro-mobility, and the proportion of the topic for regulations for micro-mobility increased as shared e-scooter services increased in recent years. In addition, the top positive words included quick, enjoyable, and easy, whereas the top negative words included threat, complaint, and ilegal, which implies that people satisfied with the convenience of e-scooter or e-scooter sharing services, but safety and parking issues should be addressed for micro-mobility services to become more active. In conclusion, this study was able to understand how issues and social trends related to e-scooters have changed, and to determine the issues that need to be addressed. Moreover, it is expected that the research framework using dynamic topic modeling and sentiment analysis will be helpful in determining social trends on various areas.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (