Text Emotion Analysis Using Deep Learning For COVID- 19 Dataset

Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy. © 2022 IEEE.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units.

Background: The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics. Methods: This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany. Results: The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging. Conclusion: Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Macroeconomic and Uncertainty Shocks' Effects on Energy Prices: A Comprehensive Literature Review

GDP, monetary variables, corruption, and uncertainty are crucial to energy policy decisions in today's interrelated world. The global energy crisis, aggravated by rising energy prices, has sparked a thorough analysis of its causes. We demonstrate the significance of categorizing research by influence channels while focusing on their implications for energy policy decisions. We investigate the growing number of studies that use GDP, inflation, central banks' characteristics, corruption, and uncertainty as critical factors in determining energy policies. Energy prices fluctuate because energy policies shift the supply–demand equilibrium. We categorise the effects and show that GDP, economic policy uncertainty, and, most notably, specific economic conditions and extreme events play a significant role in determining energy prices. We observed that energy consumption, GDP growth, and energy prices have a bidirectional, causal relationship. Still, the literature has not established which causative direction is the most significant. Taxes, interest rates, and corruption also significantly determine energy prices, although the origins of corruption have not been adequately examined. Lastly, uncertainty generally increases energy costs, but this relationship requires additional research in terms of the features of countries, conditions, and, most importantly, the theoretical backgrounds used.

Risk-Informed Decision-Making Tool for Covid-19 Community Behavior and Intervention Scenario Assessment

The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological Susceptible-Infected-Recovered-Dead (SIRD) model as a basis, this paper presents a methodology for modeling many uncertain factors impacting disease spread, ultimately to understand how a policy decision may impact the community long term. The COVID-19 Decision Support (CoviDeS) tool, utilizes an agent-based time simulation model that uses Bayesian networks to determine state changes of each individual. The model has a level of interpretability more extensive than many existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases are presented for different scenarios that demonstrate relative changes in transmission resulting from different policy decisions. Further, we will demonstrate the model's ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to other real-world scenarios of interest. Though improvements can be made in the model's accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

Classifying Anti-Mask Tweets into Misclassification vs. Rejection: A Year-Long Study

The debate over masks has played out vigorously over social media platforms such as Twitter over the course of the Covid-19 pandemic. Anti-maskers oppose the use of face masks on two philosophical grounds. First, they question their effectiveness and second, they reject them as an infringement of their personal liberties and freedoms. Both these narratives can be damaging in their own respective ways;misinformation can mislead people to abandon this simple public health measure, and rejection can incite unrest, disobedience and violence. Different policies, ranging from completely removing the tweet to simply placing a warning label, may be applied to these two types of anti-mask tweets to mitigate their damage. To facilitate these differentiated policy decisions, driven by the state of the pandemic and the surrounding social and political circumstances, this paper proposes a machine learning approach to separate anti-mask tweets into misinformation and rejection. Linguistic, social, auxiliary, and sentiment features are extracted from this corpus of tweets collected over the first year. A combination of these features is used to train ensemble and neural network classifiers. The results show that our machine learning framework can separate between misinformation and rejection tweets with a F1-score of around 0.90. These results are noteworthy because the framework can classify between two groups of tweets that share a common overall theme of anti-masking yet have only subtle differences. Moreover, the data collected over a period of one year implies that this separation is achieved even when the anti-masking rhetoric is embedded in widely varying social and political contexts. © 2022 ACM.

The Battlefront of Combating Misinformation and Coping with Media Bias

Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic, which gave rise to the first social-media infodemic, and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system that not only fact-checks information pieces provable by background knowledge, but also reason about the consistency and the reliability of subtle details about emerging events;(ii) uncover the bias and the agenda of news sources to better characterize misinformation;as well as (iii) correct false information and mitigate news biases, while allowing diverse opinions to be expressed. Participants will learn about recent trends, representative deep neural network language and multimedia models, ready-to-use resources, remaining challenges, future research directions, and exciting opportunities to help make the world a better place, with safer and more harmonic information sharing. © 2022 Owner/Author.

Public Sentiment on User Reviews about Application in Handling COVID-19 using Naive Bayes Method and Support Vector Machine

Another crisis has emerged in the shape of widespread anxiety and panic, fueled by imprecise and frequently incorrect information, during the Coronavirus pandemic. As a result, there is a critical need to address and better comprehend COVID-19's informational crisis, as well as evaluate public mood, in order to adopt effective communications and policy decisions. This study aims to classify the results of PIKOBAR's review sentiments, PIKOBAR is a Center for Information and Coordination of Diseases and Disasters in West Java. A total of 371 reviews were taken, each of which was labeled positive, negative or neutral. The data first goes through a pre-processing process before conducting a sentiment review analysis using the Naive Bayes Classifier and Support Vector Machine processes. The results from 80% testing data and 20% training data obtained the Naive Bayes accuracy rate of 75.67% and the Support Vector Machine was 71.62%. Furthermore, in the text association process, information was obtained that the PIKOBAR application users mostly talked about words 'Jabar' for positive class and the word 'aplikasi' for negative class and the word 'data' for neutral class. © 2022 IEEE.

MAGNETOFLUIDICS-ENABLED POINT-OF-CARE SARS-COV-2 DIAGNOSTICS

As the SARS-CoV-2 virus continues to mutate, global eradication of infections is unlikely, and COVID-19 is predicted to become a seasonal or endemic disease like influenza. Widespread detection of variant strains will be critical to inform policy decisions to mitigate further spread, and post-pandemic multiplexed screening of respiratory viruses will be necessary to properly manage patients presenting with similar respiratory symptoms. We have developed a portable, magnetofluidic platform for multiplexed PCR testing in <30 min. Cartridges were designed for multiplexed detection of SARS-CoV-2 with either distinctive variant mutations or with Influenza A and B and tested with clinical samples. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Note: Home Location Detection from Mobile Phone Data: Evidence from Togo

Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms. © 2022 Owner/Author.

Impact of COVID-19 pandemic to EDUCON: a bibliometric analysis

In recent years, bibliometrics has been widely used as a methodology for the analysis of scientific production, proving to be an excellent instrument for supporting scientific policy decisions. In this paper, we present a bibliometric analysis of the 12 editions of the Global Engineering Education Conference (EDUCON). Several indicators were used, namely number of submitted articles, the rate of accepted submissions, number of article citations, and the number of references per article, among other indicators. We also intend to add the comparison of the results obtained in the ten first editions held in-site and the last two editions, which have the particularity of having been carried out fully online, evaluating, by this way, the impact of the COVID-19 pandemic crisis to EDUCON. The results show an increase in the number of papers submitted and published over the years, evidencing a growing notoriety of the conference. The same growth tendency is visible regarding the number of authors involved in the conference. Regarding the impact of the online editions, data from the next edition is still required to establish better comparisons and conclusions. © 2022 IEEE.

COVID-19 Next Day Trend Forecast

Historically, weather conditions are depicted as an essential factor to be considered in predicting variation infections due to respiratory diseases, including influenza and Severe Acute Respiratory Syndrome SARS-CoV-2, best known as COVID-19. Predicting the number of cases will contribute to plan human and non-human resources in hospital facilities, including beds, ventilators, and support policy decisions on sanitary population warnings, and help to provision the demand for COVID-19 tests. In this work, an integrated framework predicts the number of cases for the upcoming days by considering the COVID-19 cases and temperature records supported by a kNN algorithm. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Air quality evaluation during COVID-19 in Southern Italy: the case study of Avellino city.

The present study relies on the air quality evaluation during COVID-19 pandemic in Avellino, described in the last years and for several consecutive years, among the worst Italian cities in this context. The main purpose of this manuscript was to investigate the effects of quarantine and lockdown measures on air pollution. The concentrations of the main atmospheric pollutants (Carbon monoxide (CO), Ozone (O3), Fine Particulate (PM2.5 and PM10), Benzene (C6H6) and Nitrogen dioxide (NO2) were recorded during the period January-December 2020 using two stationary monitoring stations (AV1 and AV2) of the Regional Environmental Protection Agency (ARPAC). During the lockdown period (March 9-May 18, 2020), results indicated significant reductions only in the levels of CO, benzene and NO2, while for PM10 the limit of 50 µg m-3 was passed 8 times for AV1 and 13 times for AV2. The results showed the not predominant role of traffic on air quality in Avellino regards to PM levels and make it necessary a serious reflection about important and not extendable decisions to improve the air quality.

Superposition of COVID-19 waves, anticipating a sustained wave, and lessons for the future.

The 2019 coronavirus (COVID-19), also known as SARS-CoV-2, is highly pathogenic and virulent, and it spreads very quickly through human-to-human contact. In response to the growing number of cases, governments across the spectrum of affected countries have adopted different strategies in implementing control measures, in a hope to reduce the number of new cases. However, 5 months after the first confirmed case, countries like the United States of America (US) seems to be heading towards a trajectory that indicates a health care crisis. This is in stark contrast to the downward trajectory in Europe, China, and elsewhere in Asia, where the number of new cases has seen a decline ahead of an anticipated second wave. A data-driven approach reveals three key strategies in tackling COVID-19. Our work here has definitively evaluated these strategies and serves as a warning to the US, and more importantly, a guide for tackling future pandemics. Also see the video abstract here https://youtu.be/gPkCi2_7tWo.