Analysis of social and psychological factors influencing the treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019.

Objective To explore the sociodemographic and psychological factors influencing the continuity of treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019 (COVID-19). Methods A total of 277 patients with chronic kidney disease who were admitted to Department of Nephrology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2020 to Mar. 2021 were enrolled and divided into 3 groups: non-dialysis group (n=102), hemodialysis (HD) group (n=108), and peritoneal dialysis (PD) group (n=67). All patients were investigated by online and offline questionnaires, including self-designed basic situation questionnaire, self-rating anxiety scale (SAS), and self-rating depression scale (SDS). The general sociodemographic data, anxiety and depression of the 3 groups were compared, and the influence of sociodemographic and psychological factors on the interruption or delay of treatment was analyzed by binary logistic regression model. Results There were significant differences in age distribution, marital status, occupation, medical insurance type, caregiver type, whether there was an urgent need for hospitalization and whether treatment was delayed or interrupted among the 3 groups (all P0.05). The average SAS score of 65 PD patients was 38.15+/-15.83, including 53 (81.5%) patients without anxiety, 7 (10.8%) patients with mild anxiety, and 5 (7.7%) patients with moderate to severe anxiety. The average SAS score of 104 patients in the HD group was 36.86+/-14.03, including 81 (77.9%) patients without anxiety, 18 (17.3%) patients with mild anxiety, and 5 (4.8%) patients with moderate to severe anxiety. There were no significant differences in the mean score of SAS or anxiety severity grading between the 2 groups (both P0.05). The mean SDS scores of 65 PD patients were 53.42+/-13.30, including 22 (33.8%) patients without depression, 21 (32.3%) patients with mild depression, and 22 (33.8%) patients with moderate to severe depression. The mean SDS scores of 104 patients in the HD group were 50.79+/-10.76, including 36 (34.6%) patients without depression, 56 (53.8%) patients with mild depression, and 12 (11.6%) patients with moderate to severe depression. There were no significant differences in mean SDS scores or depression severity grading between the 2 groups (both P0.05). The results of intra-group comparison showed that the incidence and severity of depression were higher than those of anxiety in both groups. Multivariate binary logistic regression analysis showed that high school education level (odds ratio OR=5.618, 95% confidence interval CI) 2.136-14.776, P0.01), and unmarried (OR=6.916, 95% CI 1.441-33.185, P=0.016), divorced (OR= 5.588, 95% CI 1.442-21.664, P=0.013), urgent need for hospitalization (OR=8.655, 95% CI 3.847-19.476, P0.01) could positively promote the continuity of treatment in maintenance dialysis patients under the regular epidemic prevention and control of COVID-19. In the non-dialysis group, no sociodemographic and psychological factors were found to be associated with the interruption or delay of treatment (P0.05). Conclusion Education, marital status, and urgent need for hospitalization are correlated with the continuity of treatment in patients with chronic kidney disease on maintenance dialysis.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Understanding the use of digital finance among older internet users in urban China: Evidence from an online convenience sample

Inclusive digital financial services should welcome older populations and make them beneficiaries of the digital and financial revolution. To understand older adults' experience of using digital financial tools, we conducted an online survey of 268 older internet users aged 60 or above from urban areas of 14 Chinese provinces after China's nationwide COVID-19 lockdown in 2021. Our results revealed that older internet surfers were active in digital financial activities and engaged most with activities that were highly compatible with their lifestyles. Active users significantly differed from inactive users in sociodemographics, confirming that a digital divide related to social stratification exists among older internet users. Digital finance active users were also distinguished from inactive users' attitudes and perceptions toward digital finance. Logistic regression results indicated that perceived usefulness, access to proper devices for digital finance, risk perceptions, and perceived exclusion if not using technology were associated with their adoption of these advanced tools. Older adults reported the perceived inconvenience of in-person financial services during the lockdown. They also expressed a willingness to participate in relevant training if provided. The findings of this study could help aging-related practitioners to understand older adults' engagement in digital finance and guide policy and project design in the area of financial inclusion of the aging population.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Analysis and Prediction of Student Performance Based on Moodle Log Data using Machine Learning Techniques

During the COVID-19 Pandemic, many universities in Thailand were mostly locked down and classrooms were also transformed into a fully online format. It was challenging for teachers to manage online learning and especially to track student behavior since the teacher could not observe and notify students. To alleviate this problem, one solution that has become increasingly important is the prediction of student performance based on their log data. This study, therefore, aims to analyze student behavior data by applying Predictive Analytics through Moodle Log for approximately 54,803 events. Six Machine Learning Classifiers (Neural Network, Random Forest, Decision Tree, Logistic Regression, Linear Regression, and Support Vector Machine) were applied to predict student performance. Further, we attained a comparison of the effectiveness of early prediction for four stages at 25%, 50%, 75%, and 100% of the course. The prediction models could guide future studies, motivate self-preparation and reduce dropout rates. In the experiment, the model with 5-fold cross-validation was evaluated. Results indicated that the Decision Tree performed best at 81.10% upon course completion. Meanwhile, the SVM had the best result at 86.90% at the first stage, at 25% of the course, and Linear Regression performed with the best efficiency at the middle stages at 70.80%, and 80.20% respectively. The results could be applied to other courses and on a larger e-learning systems log that has similar student activity conditions and this could contribute to more accurate student performance prediction © 2023, International Journal of Emerging Technologies in Learning.All Rights Reserved.

Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient's chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19.

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

Trends in Postpartum Depression before and during the COVID-19 Pandemic and Correlates of Treatment Choices: A Population-Based Retrospective Cohort Study

Objectives: COVID-19-related stressors - including social distancing, material hardship, increased intimate partner violence, and loss of childcare, among others - may result in a higher prevalence of depression among postpartum individuals. This study examines trends in postpartum depression in the US from 2018 to 2022, as well as correlates of treatment choices among women with postpartum depression. Method(s): 1,108,874 women aged 14-64 in the Komodo Healthcare Map with 1+ live birth between April 2018 and December 2021 and had continuous enrollment 2+ years before and 4+ months after the delivery date were included. Prevalence of depression during postpartum (within 3 months after delivery) was calculated before (April 2018-March 2020) and during (April 2020-March 2022) COVID-19. Multinomial logistic regression was used to investigate correlates of treatment choices (no treatment, medication-only, psychotherapy-only, or both). Result(s): The prevalence of postpartum depression increased from 9.7% pre-pandemic to 12.0% during the pandemic (p < 0.001). Among 119,788 women with postpartum depression in 2018-2022, 47.0% received no treatment, 35.0% received medication-only, 10.0% received psychotherapy-only, and 7.4% received both within one month following their first depression diagnosis. Factors associated with an increase in the odds of receiving medication-psychotherapy treatment (vs. no treatment) included older ages;commercial insurance coverage;lower social vulnerability index;history of anxiety or mood disorder during and before pregnancy;and being diagnosed by a nurse practitioner, physician assistant, or behavioral care practitioner (vs. physician). Similar patterns were observed for medication-only and psychotherapy-only treatments. Conclusion(s): In this large, nationally representative sample of US insured population, the prevalence of postpartum depression increased significantly by 2.3 percentage-points during the pandemic (or a relative increase of 23.7%). Nonetheless, almost half of women with postpartum depression received no treatment, and only 7.5% received both medication and psychotherapy. The study highlighted potential socioeconomic and provider variation in postpartum depression treatment.Copyright © 2023

Trends of psychiatric emergencies during the COVID-19 health crisis in Italy

Background: Despite concerns on mental health problems related to lockdowns, recent reports revealed a reduction in psychiatric admissions in Emergency Departments (ED) during the lockdown period compared with the previous year in several countries. Most of the existing studies focused on the first lockdown not considering the different phases of the COVID-19 crisis. The present study aimed to analyse differences in ED admissions for psychiatric consultation during three different phases of the COVID-19 in Italy. Method(s): Information on ED admission the Santo Spirito Hospital in Rome for psychiatric consultations were retrospectively collected. The lockdown(March-June 2020) and the post-lockdown period (June 2020-June 2021) were compared to the pre-lockdown period(January 2019-March 2020). Multinomial logistic regression(MLR) was used to assess the risk of accessing ED for psychiatric consultation during the three periods. Result(s): 3871 ED psychiatric consultations were collected. A significant reduction of psychiatric consultations in ED during the lockdown period and the post-lockdown (H 762.45;p < .001) was documented. MLR showed that compared to pre-lockdown during the lockdown and post-lockdown patients were more likely to be men (RRR 1.52;95% CI 1.10-2.12) and more often diagnosed with non-severe mental illnesses (nSMI) (relative risk ratio [RRR] 1.53, 95% CI 1.10-2.15;and 1.72, 95% CI 1.42-2.08);during the lockdown, patients were also more often diagnosed with alcohol/substance abuse(RRR 1.70;95% CI 1.10-2.65). Conclusion(s): Several changes in the clinical characteristics of psychiatric consultations during and after the lockdown emerged;these may inform clinicians and future preventive strategies among community mental health services.

Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors.

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Machine learning-based diffusion model for prediction of coronavirus-19 outbreak.

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model.

Changes in public bike usage after the COVID-19 outbreak: A survey of Seoul public bike sharing users.

When the COVID-19 pandemic swept across the world, people tended to seek more individualized and viable transportation modes, such as a bicycle. In this study, we examined the factors influencing changes in public bike sharing (PBS) in Seoul, to assess this trend post-pandemic. We conducted an online survey of 1,590 Seoul PBS users between July 30 and August 7, 2020. Using a difference-in-differences analysis, we found that participants who were affected by the pandemic used PBS 44.6 h more than unaffected individuals throughout the year. In addition, we used a multinomial logistic regression analysis to identify the factors affecting changes in PBS usage. In this analysis, the discrete dependent variables of increased, unchanged, and decreased were considered, representing the changes in PBS usage after the COVID-19 outbreak. Results revealed that PBS usage increased among female participants during weekday trips such as commuting to work and when there were perceived health benefits of using PBS. Conversely, PBS usage tended to decrease when the weekday trip purpose was for leisure or working out. Our findings offer insight into PBS user behaviors within the context of the COVID-19 pandemic and present policy implications to revitalize PBS usage.

Understanding vulnerability and resilience of Vietnamese pangasius farming in the context of the Covid-19 pandemic

In Vietnam, pangasius farming has developed rapidly in the past decades, with the aquaculture of this particular fish species proving to be very beneficial for the local economy. However, the emergence and spread of the Covid-19 pandemic and the policy responses in slowing the spread of the virus have significantly impacted the sector. This paper investigates both the vulnerability and resilience of pangasius aquaculture by identifying the various impacts of the Covid-19 pandemic on Vietnam's pangasius sector and the subsequent responses of this sector towards the issues brought forward by it. In this paper, data collected from 195 pangasius farms in Vietnam are operationalized via binary and ordinal logistic regression in an attempt to reveal the factors affecting the sector's output and the factors driving farmers' responses in the context of insecurity caused by the Covid-19 pandemic. Since the emergence of the pandemic-induced shock on the pangasius sector, farmers have encountered difficulties regarding the sale of harvests and have been confronted with lower prices for pangasius products, with the sector reporting a reduction in the sales volume and the number of buyers, as well as farmers facing disruptions in logistics and input accessibility. As a response to the impacts of the pandemic, pangasius farmers resorted to a number of coping strategies including feeding pangasius less, reducing the farming scale, delaying harvesting, and/or releasing smaller sized fingerlings. Our regressions suggest that farms signing contracts with buyers are more resilient to the reduction in output price and the reduction in number of buyers but, on the other hand, these farms seem to be more severely impacted by the adverse impacts of reduced sales volume, higher feed price and difficulty to sell harvest compared to those without contracts. Furthermore, our findings suggest that farms selling products to export traders suffered more severely from the reduction in the price of pangasius products and in the number of buyers but, at the same time, these farms seemed to have fewer problems with the reduced sales volume and the increased feed price. Interestingly, our findings also suggest a cross-vulnerability with climate change, specifically with respect to the increase in feed price and the disruption in input logistics, hereby further increasing the vulnerability of pangasius farmers.

Predicting Success: An Examination of the Predictive Validity of a Measure of Motivational-Developmental Dimensions in College Admissions.

Amid the COVID-19 pandemic, an unprecedented number of higher education institutions adopted test-optional admissions policies. The proliferation of these policies and the criticism of standardized admissions tests as unreliable predictors of applicants' postsecondary educational promise have prompted the reimagining of evaluative methodologies in college admissions. However, few institutions have designed and implemented new measures of applicants' potential for success, rather opting to redistribute the weight given to other variables such as high school course grades and high school GPA. We use multiple regression to investigate the predictive validity of a measure of non-cognitive, motivational-developmental dimensions implemented as part of a test-optional admissions policy at a large urban research university in the United States. The measure, composed of four short-answer essay questions, was developed based on the social-cognitive motivational and developmental-constructivist perspectives. Our findings suggest that scores derived from the measure make a statistically significant but small contribution to the prediction of undergraduate GPA and 4-year bachelor's degree completion. We also find that the measure does not make a statistically significant nor practical contribution to the prediction of 5-year graduation.

Factors associated with the perception of impaired learning by students from Brazilian public universities during the Covid-19 pandemic

The Covid-19 pandemic hit, in march 2020, the whole society, with restrictions on conviviality and the need for adaptations in all areas. Specifically in education, institutions temporarily interrupted their activities and got mobilized to continue their classes remotely, supported by technological ways that allow virtual meetings in real time. Given the uniqueness of the situation, not everyone in the academic area was prepared to replace face-to-face classrooms with virtual learning environments. In this context, this work sought to look into the impacts of remote teaching in the lives of students at brazilian public universities during the Covid-19 pandemic. A query was carried out with the students, through an electronic form, questioning, among other things, the feeling of loss in learning during the duration of remote teaching. The responses to this query composed a database analyzed using the statistical methodology of Binary Logistic Regression. Thus, this work aimed to adjust a statistical model to identify possible factors associated with the feeling of loss in the learning process, on the part of students from public universities, in relation to remote teaching. The results show that five covariables, selected through the stepwise process, had greater influence on the response variable. The highlighted covariables were: number of synchronous classes, student's ability to concentrate, remote interaction with the teacher, remote assistance from the teacher and age group. This result reveals the role of the teacher in the learning process.

Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

Comparison of Predictive Machine Learning Models to Predict the Level of Adaptability of Students in Online Education

With the onset of the COVID-19 pandemic, online education has become one of the most important options available to students around the world. Although online education has been widely accepted in recent years, the sudden shift from face-to-face education has resulted in several obstacles for students. This paper, aims to predict the level of adaptability that students have towards online education by using predictive machine learning (ML) models such as Random Forest (RF), K-Nearest-Neighbor (KNN), Support vector machine (SVM), Logistic Regression (LR) and XGBClassifier (XGB).The dataset used in this paper was obtained from Kaggle, which is composed of a population of 1205 high school to college students. Various stages in data analysis have been performed, including data understanding and cleaning, exploratory analysis, training, testing, and validation. Multiple parameters, such as accuracy, specificity, sensitivity, F1 count and precision, have been used to evaluate the performance of each model. The results have shown that all five models can provide optimal results in terms of prediction. For example, the RF and XGB models presented the best performance with an accuracy rate of 92%, outperforming the other models. In consequence, it is suggested to use these two models RF and XGB for prediction of students' adaptability level in online education due to their higher prediction efficiency. Also, KNN, SVM and LR models, achieved a performance of 85%, 76%, 67%, respectively. In conclusion, the results show that the RF and XGB models have a clear advantage in achieving higher prediction accuracy. These results are in line with other similar works that used ML techniques to predict adaptability levels. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Status quo and influencing factors of post-traumatic stress disorder among front-line anti-epidemic medical staff

[Background] Front-line medical staff are an important group in fighting against Coronavirus Disease 2019 (COVID-19), and their mental health should not be ignored. [Objective] This study investigates the current situation and influencing factors of post-traumatic stress disorder (PTSD) among front-line anti-epidemic medical staff during COVID-19 epidemic. [Methods] Medical staff who had participated in fighting against the COVID-19 epidemic wereselected from three grade III Class A hospitals and four grade II Class A hospitals in a city of Hubei Province by convenient sampling method in May 2020. The survey was conducted online using the Post-traumatic Stress Checklist-Civilian Version (PCL-C) as the main survey tool to investigate current situation and characteristics of PTSD among these participants. A total of 1120 questionnaires were collected, of which 1071 were valid, and the effective rate was 95.6%. [Results] Of the 1071 participants, the average age was (32.59+/-5.21) years;the ratio of male to female was 1: 5.02;the ratio of doctor to nurse was 1:5.8;nearly 70% participants came from grade III Class A hospitals;married participants accounted for 75.4%;most of them held a bachelor degree or above (86.5%);members of the Communist Party of China (CPC) accounted for 22.9%;50.9% had junior titles;the working years were mainly 5-10 years (42.8%);more than 80.0% participants volunteered to join the front-line fight;95.1% participants received family support;43.0% participated in rescue missions;78.1% participants fought the epidemic in their own hospitals;more than 60% participants considered the workload was greater than before;34.4% participants fought in the front-line for 2-4 weeks, and 23.5% participants did for more than 6 weeks. There were 111 cases of positive PTSD syndromes (PCL-C total score >=38) with an overall positive rate of 10.4%, and the scores of reexperience [1.40 (1.00, 1.80)] and hypervigilance [1.40 (1.00, 2.00)] were higher than the score of avoidance [1.14 (1.00, 2.57)]. The results of univariate analysis revealed that PTSD occurred differently among participants grouped by age, political affiliation, working years, anti-epidemic activities location, accumulated working hours in fighting against COVID-19, having child parenting duty, voluntariness, family support, whether family members participated in front-line activities, and rescue mission assignment (P<0.05). The results of logistic regression analysis showed that the incidence rates of reporting PTSD syndromes in medical personnel aged 31-40 years (OR=0.346, 95%CI: 0.164-0.730) and aged 41 years and above (OR=0.513, 95%CI: 0.319-0.823) were lower than that in those aged 20-30 years;the incidence rates of reporting PTSD syndromes in medical staff who were CPC members (OR=0.499, 95%CI: 0.274-0.909), volunteered to participate (OR=0.584, 95%CI: 0.360-0.945), and received family support (OR=0.453, 95%CI: 0.222-0.921) were lower than those did not (P<0.05);the incidence rates of reporting PTSD syndromes among medical workers who had child parenting duty (OR=2.372, 95%CI: 1.392-4.042), whose family members participated in front-line activities (OR=1.709, 95%CI: 1.135-2.575), and who participated in rescue missions (OR=1.705, 95%CI: 1.133-2.565) were higher than those who did not (P<0.05). [Conclusion] The positive PTSD syndrome rate is 10.4% in the front-line anti-epidemic medical staff. Age, political affiliation, voluntariness, family support, having child parenting duty, with a family members participating in the fight, and rescue mission assignment are the influencing factors of PTSD.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.