Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning models.

The present research deals with sentiment analysis performed with Microsoft Azure Machine Learning Studio to classify Facebook posts on the Greek National Public Health Organization (EODY) from November 2021 to January 2022 during the pandemic. Positive, negative and neutral sentiments were included after processing 300 reviews. This approach involved analyzing the words appearing in the comments and exploring the sentiments related to daily surveillance reports of COVID-19 published on the EODY Facebook page. Moreover, machine learning algorithms were implemented to predict the classification of sentiments. This research assesses the efficiency of a few popular machine learning models, which is one of the initial efforts in Greece in this domain. People have negative sentiments toward COVID surveillance reports. Words with the highest frequency of occurrence include government, vaccinated people, unvaccinated, telephone communication, health measures, virus, COVID-19 rapid/molecular tests, and of course, COVID-19. The experimental results disclose additionally that two classifiers, namely two class Neural Network and two class Bayes Point Machine, achieved high sentiment analysis accuracy and F1 score, particularly 87% and over 35%. A significant limitation of this study may be the need for more comparison with other research attempts that identified the sentiments of the EODY surveillance reports of COVID in Greece. Machine learning models can provide critical information combating public health hazards and enrich communication strategies and proactive actions in public health issues and opinion management during the COVID-19 pandemic.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms.

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Estimating Consumers' Knowledge and Attitudes Towards Over-The-Counter Analgesic Medication in Greece in the Years of Financial Crisis: The Case of Paracetamol.

INTRODUCTION: Non-prescription over-the-counter (OTC) drugs are widely used by patients to control aches, pain, and fever. One of the most frequently used OTC medications worldwide is paracetamol (acetaminophen). The aim of the present study was to fill the current knowledge gap regarding the beliefs and attitudes of people in Greece associated with the use of paracetamol during the years of financial crisis. METHODS: The present study employed a sample of individuals visiting community pharmacies in the second largest city of Greece, Thessaloniki. All participants anonymously answered a questionnaire regarding their beliefs and characteristics of paracetamol consumption. Their answers were then statistically analyzed. RESULTS: The generic paracetamol compound was shown to be more well known than the original. A significant percentage of participants, ranging between 9.9% and 33.7%, falsely believed that certain medications [mainly non-steroidal anti-inflammatory drugs (NSAIDs)] contained paracetamol. Participants' age, level of education, and gender were shown to be predictive of this false belief. Additionally, 11.1% of participants believed that the maximum allowed daily dose of paracetamol was higher than the correct one. Better educated individuals were less likely to consume alcohol in parallel with paracetamol (odd ratio 0.230, 95% confidence interval 0.058-0.916, P = 0.037). CONCLUSION: Paracetamol is commonly used, both in its original and generic forms. However, a significant number of individuals confuse it with NSAIDs. Age, level of education, and gender are important determinants of the characteristics of paracetamol consumption. It seems that patients prefer to take paracetamol on their own decision during the financial crisis.