Yikes! The Effect of Incidental Disgust and Information on Public Attitudes During the COVID-19 Pandemic.

Existing research has focused extensively on the role of emotions such as anger, fear, and enthusiasm in explaining public opinion, but less is known about the importance of disgust, an innate disease-related emotion. To study the independent and joint effects of disgust and information, I draw on the case of the COVID-19 pandemic. I demonstrate that experimentally induced incidental disgust and exposure to information about how to flatten the curve of the COVID-19 cases have distinctive effects on political, racial, and health attitudes. Independently, exposure to information affects preferences only for restrictive policies to fight the spread of the virus. In contrast, the stand-alone effect of incidental disgust, as well as its joint effect with exposure to information, are responsible for attitude change toward both pandemic-relevant and irrelevant policies, Asian minorities, and prevention measures. Importantly, the study finds that citizens respond symmetrically to disgusting stimuli and information across degrees of political awareness, ideology, partisan affiliation, and trait authoritarianism. The results draw attention to the far-reaching implications of disgust on public opinion under threatening conditions.

A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions

This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular, we show that economic policy variables are of importance for the main epidemiological parameters derived from the policy learning model. In an empirical second-stage application, we further investigate the underlying dynamics between the epidemiological parameters and household debt repayments, a key economic variable, in the UK. Results show no Bayesian learning, although a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact. Therefore, suboptimal learning is taking place.

Learning in a post-truth world

Misinformation has emerged as a major societal challenge in the wake of the 2016 U.S. elections, Brexit, and the COVID-19 pandemic. One of the most active areas of inquiry into misinformation examines how the cognitive sophistication of people impacts their ability to fall for misleading content. In this paper, we capture sophistication by studying how misinformation affects the two canonical models of the social learning literature: sophisticated (Bayesian) and naive (DeGroot) learning. We show that sophisticated agents can be more likely to fall for misinformation. Our model helps explain several experimental and empirical facts from cognitive science, psychology, and the social sciences. It also shows that the intuitions developed in a vast social learning literature should be approached with caution when making policy decisions in the presence of misinformation. We conclude by discussing the relationship between misinformation and increased partisanship and provide an example of how our model can inform the actions of policymakers trying to contain the spread of misinformation. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Application of Machine Learning in the Fight against the COVID-19 Pandemic: A Review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Method(s): This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim(s): This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion(s): ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.Copyright © 2022 Sciendo. All rights reserved.

Intervention uncertainty, household health, and pandemic.

This study builds a policy choice model wherein household health status responds to the lockdown during the COVID-19 pandemic. Considering an exogenous policy-decision date, the model implies that the government should maintain the current policy if the perceived effects on infection are below a certain threshold. Specifically, the threshold is determined by policy uncertainty and household concerns regarding health service provision, which further controls the announcement effects of the lockdown. Higher policy uncertainty and concerns regarding health services will diminish the positive impact of the lockdown on household health status.

Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities.

Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.

Diagnosis of Skin Cancer by Using Fuzzy-Ann Expert System with Unification of Improved Gini Index Random Forest-Based Feature

In addition to being one of the most widespread and lethal diseases in the world, skin cancer is also one of the most common types of cancer. However, due to its complexity and fuzzy nature, the clinical diagnosis process of any disease, including skin cancer, prostate cancer, coronary artery disorders, diabetes, and COVID-19, is frequently accompanied by doubt. In order to address the uncertainty and ambiguity surrounding the diagnosis of skin cancer as well as the heavier burden on the overlay of the network nodes of the fuzzy neural network system that frequently occurs due to insignificant features that are used to predict or diagnose the disease, a fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm was proposed in this work. A Greater Gini Index Out of the 30 features in the dataset, the five most fitting features of the diagnostic Wisconsin breast cancer database were chosen using a random forest-based feature importance measure algorithm. Two sets of classification models were created using the logistic regression, support vector machine, k-nearest neighbour, random forest, and Gaussian naive Bayes learning algorithms. As a result, models for classification that included all features (30) and models that only used the top five features were used. The efficacy of the two sets of categorization models was assessed, and the results of the assessment were compared. The comparison's results show that the models with the fittest features outperformed those with the most complete features in terms of accuracy, sensitivity, and sensitivity. A fuzzy neural network-based expert system was therefore developed, utilising the five best features, and it achieved 99.83 percent accuracy, 99.86 percent sensitivity, and 99.64 percent specificity. The system built in this study also stands to be the best in terms of accuracy, sensitivity, and specificity when compared to prior research that used fuzzy neural networks or other applicable artificial intelligence techniques on the same dataset for the diagnosis of skin cancer. The z-test was also performed, and the test result demonstrates that the system has significantly improved accuracy for early skin cancer diagnosis. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

An Improved Efficiency in Envisioning the Personage Traits over Online Social Media based on Indian Metrics during Pandemic using Novel Naive Bayes Classifier Algorithm Comparing with Logistic Regression Algorithm

Aim: The primary aim of this research is to increase the intensity percentage of personage traits detection to reveal the impact of coronavirus on Twitter users by utilizing machine learning classifier algorithms by comparing Novel Naive Bayes Classifier algorithm and Logistic Regression algorithm. Material(s) and Method(s): Naive Bayes Classifier algorithm with test size=10 and Logistic Regression algorithm with test size=10 was estimated several times to envision the efficiency percentage with confidence interval of 95% and G-power (value=0.8). Naive Bayes classifier compares whether a specific feature in a class is unrelated to another feature. A logistic regression model predicts the probability of an item belonging to one group or another. Results and Discussion: Naive Bayes algorithm has greater efficiency (86%) when compared to Logistic Regression efficiency (60%). The results achieved with significance value p=0.169 (p>0.05) shows that two groups are statistically insignificant. Conclusion(s): Naive Bayes Algorithm executes remarkably greater than the Logistic Regression algorithm. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Student Stress Prediction Using Machine Learning Algorithms And Comprehensive Analysis

Student performance is most often hampered by mental health difficulties. Students' motivation, attention, and social ties can all be impacted by mental illness, all of which are key factors in their academic achievement. Due to the novel coronavirus pandemic, many institutions and colleges throughout the world have resorted to online learning. Despite widespread use of emergency remote learning (ERL) in higher education during the COVID-19 pandemic, little is known about the elements that influence student satisfaction and stress levels in this innovative learning environment in a crisis. Our research intends to provide a timely assessment of the COVID-19 pandemic's impact on college students' mental stress level employing machine learning algorithms to predict the stress faced by students based on their academic routines. Data collected through student surveys relating to a lot of factors such as time spent on studying, social media, health and fitness etc. provide a strong basis to determine students stress levels and via supervised machine learning algorithms predictions are done on the academic stress by analyzing the prime factors affecting the issue at hand. Various ML models such as Naive Bayes, Random Forest, Artificial Neural Networks (ANN) etc. have been employed and a comprehensive comparison is performed with the proposal of the most optimum algorithm for the prediction of stress level. Copyright © 2022, Anka Publishers. All rights reserved.

Sentimental Analysis Of Covid-19 Vaccination Tweets

In March 2020, Coronavirus disease was officially announcedas a pandemic all over the world by the World Health Organization(WHO) Since then, the whole pharmaceutical world is in a state of warwith COVID-19 and has a responsibility to provide its vaccine for theentire world as soon as possible. The coronavirus outbreak has broughtunprecedented measures, which forced the authorities to make decisionsrelated to the installation of lockdown in the areas most hit by the pan-demic. Social media has been an important support for people whilepassing through this difficult period. Tweets collected, analyzed, and in-cluded in the media reports. Based on the analysis, it can be seen thatMost tweets are neutral, while the number of compatible tweets exceedsthenumberoftweetsagainsttweets.[5, 15, 17]Intermsofnews,itisconsidered that the occurrence of tweets follows the practice of events.Moreover, the proposed method can be used in a long-term monitoringcampaign that can help governments to establish appropriate commu-nication systems and to evaluate them in order to provide clear andadequate information to the general public, which can increase publicconfidenceinthevaccinecampaign.Thedatasetistrainedonamachinelearningmodeltoclassifytheopinionsof peopleonthevaccinationpro-cess.ThealgorithmsusedareBERT,SVMandNaiveBayes.[1,7]. Copyright © 2022, Anka Publishers. All rights reserved.

SENTIMENT ANALYSIS OF PUBLIC TWEETS ON COVID-19 VACCINATION

During the COVID-19 pandemic, social media have been used for people to communicate and express their views. Exploring and examining such people's views can improve the government’s response time to those issues. This proposed approach is to bring off sentiment analysis on tweets regarding vaccines of COVID-19. The Data Annotation was performed using VADER. A natural language toolkit was used to pre-process the tweets and Term Frequency Inverse Document Frequency (TF-IFD) and count vectorization is used for data vectorization and sentiment analysis was carried out using 14 different machine learning classifiers. We detect that the unigram model surpasses the bigram and trigram model, Also the model utilizing the (TF-IFD) has high accuracy than the model using the count vectorizer. Out of 14 machine learning algorithm, Passive-Aggressive has the best accuracy of 85% with TF-IFD class and 84.21% for count vectorization class. For all the classifiers standard deviation of the unigram model is 1 except Gaussian Naive Bayes whose value is 1.18.

Detection of COVID-19 Patients Requiring Escalation to ICU Status Using a NaI&Die;ve Bayes Classifier

Background. A naive Bayes classifier is a popular tool used in assigning variables an equal and independent contribution to a binary decision. With respect to COVID-19 severity, the naive Bayes classifier can consider different variables, such as age, gender, race/ethnicity, comorbidities, and initial laboratory values to determine the probability a patient may need to be admitted or transferred to an intensive care unit (ICU). The aim of this study was to develop a screening tool to detect COVID-19 patients that may require escalation to ICU status. Methods. Patients hospitalized with COVID-19 were gathered from the end of March 2020 to the end of May 2020 from four hospitals in our metropolitan area. We began searching for potential variables to include in the classification model using chi-square analysis or calculating the optimal cutpoint to separate ICU and non-ICU status. After identifying significant variables, we began using standard procedures to construct a classifier. The dataset was split 7:3 to create samples for training and testing. To appraise the model's performance, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and the Matthew's correlation coefficient (MCC) were calculated. Results. A total of 574 COVID-19 patients were included in the study. There were 402 patients in the training sample and 172 patients in the testing sample. The naive Bayes classifier demonstrated an overall accuracy result of 75.6% (95% CI;68.5% - 81.8%) using the 14 variables listed in Table 1. The model was able to correctly classify 84.9% of ICU status patients (sensitivity), but only 54.7% of non-ICU status patients (specificity). The PPV and the NPV were 80.1% and 61.7%, respectively. The AUC was 0.717 (95% CI;0.629 - 0.805) and the MCC was 0.410. Conclusion. Our naive Bayes classifier operates by recognizing certain aspects of severe COVID-19 cases and looking for the probability of the variables in said patients. We present a classification model that potentially could be used alongside other tools to screen patients with COVID-19 early in their hospital course to identify those needing escalation to ICU level care.

A computational approach in the diagnostic process of COVID-19. Synergy between the laboratory and emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic.According to the CDC, RT-PCR in respiratory samples is the gold standard for confirming the disease, although it has practical limitations as time-consuming procedures and a high rate of false-negative results. Based on data collected at Careggi Hospital from April 7th-30th 2020,we aim to assess the accuracy of a COVID-19 diagnosis through classification methods based on blood tests and information collected at the ED. 971 pts with pre-specified features of suspected COVID-19 were enrolled;physicians prospectively dichotomized patients in COVID-19 likely/unlikely based on clinical features plus results of bedside imaging.Considering the limits of each method to classify a case COVID-19 positive, further evaluation was performed to form the COVID-19 final diagnosis, established after independent clinical review of 30-day follow-up data. Several classifiers were implemented, both parametric (Logistic Regression, LR;Quadratic Discriminant Analysis, QDA) and non-parametric (Random Forest, RF;Support Vector Machine;Neural Networks;K-nearest neighbour;Naive Bayes). Log transform was applied to some of the covariates and results compared with non transformed data.The dataset was divided in training and validation sets.Results based on validation sample show an AUC>0.8 for all classifiers. Best results are obtained applying RF, LR and QDA to a rebalanced sample using the SMOTE techniques on the log transformed data, showing an AUC of 0.890 (LR),0.896 (QDA) and 0.864 (RF). In parallel, best Sens and Spec are obtained via the above methods, the highest chieved by the LR (Sens 0.696;Spec 0.877). The rather high rate of false negative seems to be a feature inherently characterizing this classification problem.Good discriminatory power was shown for: WBC, Neut, AST, LDH, PCR, Na, IL-6 plus symptoms' information. Parametric models have the additional advantage of allowing a scientific interpretation.The performance of the classifiers with respect to the physician's gestalt and data validation are ongoing. The proposed classifiers show a good level of Sens.To improve Spec, a 3-level classification can be implemented;this tool can help in taking decisions when time and resources are scarce.

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.