ABSTRACT
The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.
ABSTRACT
INTRODUCTION: The COVID-19 pandemic has led to drastic measures being implemented for the management of surgical patients across all health services worldwide, including the National Health Service in the United Kingdom. It is suspected that the virus has had a detrimental effect on perioperative morbidity and mortality. Therefore, the aim of this study was to assess the impact of the COVID-19 pandemic on these outcomes in emergency general surgical patients. METHODS: Emergency general surgical admissions were included in this retrospective cohort study in one of the COVID-19 hotspots in the South East of England. The primary outcome was the 30-day mortality rate. Secondary outcomes included the length of stay in hospital, complication rate and severity grade and admission rates to the ITU. RESULTS: Of 123 patients, COVID-19 was detected in 12.2%. Testing was not carried out in 26%. When comparing COVID-positive to COVID-negative patients, the mean age was 71.8 + 8.8 vs. 50.7 + 5.7, respectively, and female patients accounted for 40.0 vs. 52.6%. The 30-day mortality rate was 26.7 vs. 3.9 (OR 6.49, p = 0.02), respectively. The length of stay in hospital was 20.5 + 22.2 vs. 7.7 + 9.8 (p < 0.01), the rate of complications was 80.0 vs. 23.7 (OR 12.9, p < 0.01), and the rate of admission to the ITU was 33.3 vs. 7.9% (OR 5.83, p = 0.01). CONCLUSION: This study demonstrates the detrimental effect of COVID-19 on emergency general surgery, with significantly worsened surgical outcomes.
ABSTRACT
The rapid increase in COVID-19 cases has become the symbol of fear, anxiety, and panic among people around the globe. Mass media has played an active role in community education by addressing the health information of this pandemic. People interact by sharing their ideas and feelings through social media platforms. There is a considerable need to implement different measures and better perceive COVID-19 pertinent facts and information by demystifying public sentiments. In this study, the Quarantine Life dataset of thousand tweets is based on #Quarantine, #Quarantine Days, #Quarantine Life, #My Pandemic Plan, and #Quarantine and Chill from January to September 2020 has been collected from Twitter. The extracted data have been scrubbed through preprocessing techniques. The sentiments and topics extracted from tweets have been analyzed through the TEXT BLOB, VADER, and AFFIN approach. Results show that people were distressed and fearful due to the COVID-19 pandemic. However, most people enjoyed by playing games, watching movies, and reading books during the lockdown period. According to the present meta-analysis, physical activity interventions are beneficial for patients with dementia in terms of cognition. The proposed framework illustrates the insight impact of COVID-19 on human physiological and mainly focuses on the evaluation of sentiment dynamics at the topical level.
ABSTRACT
The subject of variance estimation is one of the most important topics in statistics. It has been clarified by many different research studies due to its various applications in the human and natural sciences. Different variance estimators are built based on traditional moments that are especially influenced by the existence of extreme values. In this paper, with the presence of extreme values, we proposed some new calibration estimators for variance based on L-moments under double-stratified random sampling. A simulation study with COVID-19 data is performed to evaluate the efficiency of the proposed estimators. All results indicate that the proposed estimators are often superior and highly efficient compared to the existing traditional estimator.