A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study

The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in Intensive Care Unit (ICU) beds. We analyse time series of confirmed cases, hospitalised patients, ICU bed occupancy, recovered patients and deaths. We employ state-of-the-art forecasting algorithms, such as ARTXP, ARIMA, SARIMAX, and Multivariate Regression models. We combine these into three forecasting models culminating to a tri-model approach in time series analysis and compare them. The results of this study show that the combination of ARIMA with SARIMAX is more accurate for the majority of the investigated regions in short term 1-week ahead predictions, while Multivariate Regression outperforms the other two models for 2-weeks ahead predictions. Finally, for the medium term 3-weeks ahead predictions the Multivariate Regression and ARIMA with SARIMAX show the best results. We report on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), R-squared ((Formula presented.)), and Mean Absolute Error (MAE) values, for one-week, two-week and three-week ahead predictions for ICU bed requirements. Such timely insights offer new capabilities for efficient management of healthcare resources. © 2023 by the authors.

Social Media Sentiment Analysis Related to COVID-19 Vaccinations

The wake of the COVID-19 pandemic has yet again highlighted how vital immunization is for public health. Despite the dramatic spread of SARS-CoV-2 and its variants, there is a rising trend of people refusing to be vaccinated. As a result, governments and health experts must gather and understand public ideas and perceptions about vaccines to design engagement and education efforts about vaccine advantages. Sentiment analysis is a common method for acquiring a broad picture of public opinion, that enables the classification of people as those who are in favor or against vaccination, as well as the determination of the factors that influence their attitudes and beliefs. The purpose of this chapter is to describe the general approach to sentiment analysis in the context of vaccinations and review its different use cases. The chapter's experimental component integrates the utilization of a dataset retrieved from Kaggle, which contains COVID-19 vaccine-related Twitter data. When attempting to perform sentiment analysis, certain methodological steps need to be considered after data collection, including data pre-processing, technique selection and model construction, as well as model evaluation and results interpretation. Both supervised and unsupervised sentiment analysis methods are investigated in the model construction step, with the former involving the implementation of Support Vector Machines and Logistic Regression algorithms, and the latter involving the use of TextBlob and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tools. The performance of each algorithm and tool is evaluated, as is the performance of each sentiment detection approach in order to select the best performing one. Social media platforms have become a common source of information and misinformation regarding vaccines. Our effort aims to emphasize the importance of mining such readily available public attitudes, as well as forecast opinions and reactions related to vaccine uptake in near real-time. Such insights could be critical in dealing with health emergency situations like the ongoing coronavirus pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Media Sentiment Analysis Related to COVID-19 Vaccinations

The wake of the COVID-19 pandemic has yet again highlighted how vital immunization is for public health. Despite the dramatic spread of SARS-CoV-2 and its variants, there is a rising trend of people refusing to be vaccinated. As a result, governments and health experts must gather and understand public ideas and perceptions about vaccines to design engagement and education efforts about vaccine advantages. Sentiment analysis is a common method for acquiring a broad picture of public opinion, that enables the classification of people as those who are in favor or against vaccination, as well as the determination of the factors that influence their attitudes and beliefs. The purpose of this chapter is to describe the general approach to sentiment analysis in the context of vaccinations and review its different use cases. The chapter’s experimental component integrates the utilization of a dataset retrieved from Kaggle, which contains COVID-19 vaccine-related Twitter data. When attempting to perform sentiment analysis, certain methodological steps need to be considered after data collection, including data pre-processing, technique selection and model construction, as well as model evaluation and results interpretation. Both supervised and unsupervised sentiment analysis methods are investigated in the model construction step, with the former involving the implementation of Support Vector Machines and Logistic Regression algorithms, and the latter involving the use of TextBlob and Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tools. The performance of each algorithm and tool is evaluated, as is the performance of each sentiment detection approach in order to select the best performing one. Social media platforms have become a common source of information and misinformation regarding vaccines. Our effort aims to emphasize the importance of mining such readily available public attitudes, as well as forecast opinions and reactions related to vaccine uptake in near real-time. Such insights could be critical in dealing with health emergency situations like the ongoing coronavirus pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case Studies in English and Greek Language

SARS-CoV-2 and its mutations are spreading around the world, threatening the human population with millions of infections and deaths. Vaccines are considered the main available weapon at hand to mitigate the spread. As a result, the development of efficient systems to understand and supervise the information dissemination, as well as the evolution of sentiments towards vaccines is critical. The goal of this research was to build and apply a supervised learning approach to monitor the dynamics of public opinion on COVID-19 vaccines using Twitter data. 1,394,535 and 61,077 tweets about COVID-19 vaccines, respectively in English and Greek, were collected, classified based on sentiment polarity and analyzed over time to gain insights into sentiment trends. Our findings reveal that overall negative, neutral, and positive sentiments were at 36.5%, 39.9%, and 23.6% in the English language dataset, respectively, whereas overall negative and non-negative sentiments were at 60.1% and 39.9% in the Greek language dataset. Policymakers and health experts could take into consideration social media sentiment analysis alongside other ways of evaluating public sentiment. Social media users are actively seeking and sharing information about pandemic-related topics, allowing governments to use social media to develop effective crisis management strategies, better inform the public with accurate and reliable news, and alleviate disease-specific concerns. © 2022, IFIP International Federation for Information Processing.

Sentiment Analysis on COVID-19 Twitter Data: A Sentiment Timeline

COVID-19 has been one of the most dominant discussion topics on Twitter since 2019. Users express their opinions representing public sentiment on the topic. This paper presents a sentiment timeline of Twitter users, regarding COVID-19 vaccines. This work raises concerns about the extracted information with regards to sentiment analysis, the dominance of each sentiment and its influential power. During the implementation of the analysis, several datasets were examined for the creation of the model. Various algorithms were employed with Random Forest performing best and therefore selected for training the model, achieving an accuracy of 91.5%. Our findings indicate that the majority of Twitter users are positive regarding COVID-19 vaccines and support WHO’s recommendations. Negative tweets comprising the minority of the tweets, appear to have a higher influential power with their retweet rates, outperforming positive and neutral sentiments. © 2022, IFIP International Federation for Information Processing.