Extracting Drug-Related Tweets in COVID-19 Pandemic

Numerous comments from various world regions have been posted during the COVID-19 outbreak regarding the impact of drug use on the COVID-19 disease. Alongside this, this paper proposes a method for extracting drug-related tweets from the COVID-19 tweets dataset. Initially, using the Addiction Center and Oxford databases, a lexicon of drug-related words and phrases is proposed. Then, incremental revisions are made to this lexicon to enhance the accuracy, recall, and F1 score evaluation metrics. The final results demonstrate that the proposed lexicon is precise and accurate. © 2022 IEEE.

Extracting Feelings of People Regarding COVID-19 by Social Network Mining

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public's ideas and points of view regarding this subject. In this regard, to extract the public's point of view in various countries at the early stages of this outbreak, a dataset of Coronavirus-related tweets in the English language has been collected, which consists of more than two million tweets starting from 23 March until 23 June 2020. To this end, we first use a lexicon-based approach with the GeoNames geographic database to label each tweet with its location. Next, a method based on the recently introduced and widely cited Roberta model is proposed to analyse each tweet's sentiment. Afterwards, some analysis showing the frequency of the tweets and their sentiments is reported for each country and the world as a whole. We mainly focus on the countries with Coronavirus as a hot topic. Graph analysis shows that the frequency of the tweets for most countries is significantly correlated with the official daily statistics of COVID-19. We also discuss some other extracted knowledge that was implicit in the tweets. © 2022 World Scientific Publishing Co.

Analyzing the Impact of COVID-19 on Economy from the Perspective of User's Reviews

One of the most important incidents in the world in 2020 is the outbreak of the Coronavirus. Users on social networks publish a large number of comments about this event. These comments contain important hidden information of public' opinion regarding this pandemic. In this research, a large number of Coronavirus- related tweets are considered and analyzed using natural language processing and information retrieval science. Initially, the location of the tweets is determined using a dictionary prepared through the Geo-Names geographic database, which contains detailed and complete information of places such as city names, streets, and postal codes. Then, using a large dictionary prepared from the terms of economics, related tweets are extracted and sentiments corresponded to tweets are analyzed with the help of the RoBERTa language-based model, which has high accuracy and good performance. Finally, the frequency chart of tweets related to economy and their sentiment scores (positive and negative tweets) is plotted over time for the entire world and the top 10 economies. From the analysis of the charts, we learn that the reason for publishing economic tweets is not only the increase in the number of people infected with the Coronavirus but also imposed restrictions and lockdowns in countries. The consequences of these restrictions include the loss of millions of jobs and the economic downturn. © 2021 IEEE.

Over 60 years of age as an independent prognostic factor of in-hospital mortality among COVID-19 patients: a cohort study in an Iranian high-incidence area.

OBJECTIVES: COVID-19 continues to cause devastation throughout the world. Various factors influence the perioperative course and prognosis of COVID-19. This study aims to collate the independent prognostic factors among hospitalised COVID-19 patients in east Iran. STUDY DESIGN: In this cohort study, all patients with a confirmed diagnosis of COVID-19 between 19 February 2020 and 1 August 2020 who were admitted to nine public hospitals of South Khorasan province, Iran, were enrolled. METHODS: Univariate analysis (chi-square [χ2], and Mann-Whitney U test) and multiple logistic regression were performed. RESULTS: This study included 1290 participants; 676 patients (52.4%) were male. A total of 1189 (92.2%) recovered, and 101 (7.8%) died. The results show that in-hospital mortality increases with advanced age (the optimal cut-off point = 62 years). The following three variables were shown to have the most significant role in in-hospital mortality: age >60 years (odds ratio [OR] = 8.01, 95% confidence interval [CI] 4.8-13.35), shortness of breath (OR = 2.65, 95% CI: 1.4-69.17) and atypical radiological manifestations in a chest X-ray on admission (OR = 2.16, 95% CI: 1.3-28.64). In the univariate analysis, associated comorbidities, such as cardiovascular diseases, influenced the in-hospital mortality rate, while the same could not be replicated in the multiple variable analysis. CONCLUSIONS: This study revealed the potential predictors of COVID-19 and highlighted the need to be cautious with advanced age and heightened clinical symptoms at the time of admission.

Investigating the relationship between corona anxiety and nursing care behaviors working in corona's referral hospitals

Objectives The prevalence of Coronavirus and its health-related psychosocial consequences is one of the most important human social events of the 21st century. Nurses, due to close contact with patients, are vulnerable to be infected with Covid-19. Therefore, they face severe psychological consequences. This study aimed to determine the relationship between Corona’s anxiety and nursing care behaviors in working in Corona referral hospitals in Kerman in 2020. Methods The present study is cross-sectional descriptive-correlational research. Sampling was per-formed by the census method. A total of 166 nurses entered the study. In the present study, three demographic questionnaires, the Corona Disease Anxiety Scale (CDAS) and Caring Behaviors Inventory (CBI) were used. The analysis was done using Descriptive and Inferential statistics SPSS V. 18 software Results The overall score of Corona anxiety among the nurses was 21.39±9.8, and the overall score of the nursing behavior of the studied nurses was 109.7±4.2 with a range of 94 to 118. Spearman’s correlation coefficient showed that there was no significant relationship between corona anxiety and caring behaviors. Conclusion The present study showed that nurses working in corona wards suffer from moderate anxiety, and the level of caring behaviors provided by nurses was optimal. According to the current study findings, it is suggested that during the outbreak of emerging and epidemic diseases, to reduce nursing staff’s anxiety, coping strategies and resilience skills, and problem-solving, managers should pay more attention. © 2020, Iran University of Medical Sciences. All rights reserved.