Perceived Attitudes Toward Vaccines, Trust and Vaccination Behaviors

Objectives: The decision-making process for taking vaccination is influenced by a multitude of factors such as individual beliefs concerning vaccinations, trust in contextual forces, and sociodemographic. This study established a model to understand the relationship between people's beliefs in the safety, importance and effectiveness of vaccines, their trust in the medical advice from the government and doctors and their behaviors of having their children vaccinated from infectious diseases in low-and-middle-income countries (LMIC). Method(s): We structured a structural equation model with two latent variables, Motivation and Trust, and their relationships with the vaccination taking behavior. Motivation is constructed by people's beliefs in the safety, importance and effectiveness of vaccines and trust is constructed by people's trust in government, medical providers and scientists. This study used the 2018 Wellcome Global Monitor dataset and focused on people in 80 LMIC. The countries were divided into eight geographic regions: Eastern Africa, Central & Southern Africa, Norther Africa & Middle East, Western Africa, Central Asia, Southeast Asia, South Asia and Southern& Eastern Europe. Result(s): The latent variable Motivation is significantly positively associated with parental vaccination behaviors in all geographic areas except for South Asia and Western Africa. South Asia is the only area where the trust in government and medical system, providers had a significant association with vaccination behavior and such association is positive. Conclusion(s): In most LMIC, positive attitudes about vaccines are associated with an improved vaccine rate. Increasing people's belief in vaccines' importance, safety and effectiveness will be essential both for boosting vaccination rates and scaling up a vaccine for COVID-19. In South Asia, trust in the government and the public health system are important in deciding taking vaccines. In these countries, policymakers need to think of ways to improve people's trust in the public health system and further effectively communicate important health messages.Copyright © 2023

The Impact of the COVID-19 Pandemic on Retrenchment, Vaccinations, and Global Happiness

COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19;the rate of vaccination and vaccine hesitancy;and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis. © 2023 IEEE.

Profiling Inconsistent/Non-Adopters of Infection Prevention Behaviours during the Covid-19 Pandemic for Targeted Public Health Education: A Population Study

Intro: With the relentless waves of coronavirus disease 2019(COVID-19), there is a need for widespread community adoption of infection prevention(IP) measures including hand hygiene, use of face masks, and staying at home when unwell. Understanding the profile of individuals who do not consistently practice IP can help target public health education. Method(s): We conducted a nationally-representative population survey from November 2020 to January 2021. Households were randomly selected from a proportionately stratified national census. The household member with the most recent birthday was invited to complete the survey. Three questions on a 5-point Likert-scale(never-rarely-occasionally-often-always) assessed IP behaviours(hand hygiene, face mask use when having a cough/cold, staying at home when having a cold/flu) before and during the pandemic. A multivariable logistic regression model was constructed to assess factors associated with the non- or inconsistent("never-rarely-occasionally") adoption of any of the three IP behaviours during the pandemic. Finding(s): Mean age of 2004 respondents was 44.5(SD 15.0) years, with 52% females and 65% being highly educated (diploma/degree holders). Although 12% reported consistently("often-always") adopting all 3 IP behaviours pre-pandemic, the majority(n=1752, 87%) reported doing so during the pandemic. After adjusting for age, educational level, and presence of chronic illness, males(AOR 1.71 [95%CI 1.30, 2.25], Chinese(AOR 1.48 [1.07, 2.05]), low-adopters of healthy lifestyle(AOR 1.59 [1.03, 2.45]) and those who did not or inconsistently adopted IP behaviours pre-pandemic(AOR 8.92 [3.28, 24.27]) were more likely not to or inconsistently adopt the 3 IP behaviours during the pandemic. Discussion(s): During the ongoing pandemic, educational messages and information channels on IP measures could be more targeted at males and Chinese. Additionally, the promotion of healthy lifestyle and consistent adoption of IP behaviours during non-pandemic times is critical for consistent adoption of IP behaviours during pandemics. Conclusion(s): Males, Chinese, and low-adopters of healthy lifestyle and IP behaviours pre-pandemic do not consistently practice IP during the pandemic.Copyright © 2023

Comparative Study of Various Lightweight Mask Recognition Algorithms

As an important line of defense against novel coronavirus, masks can effectively reduce the risk of novel coronavirus infection. In this paper, three algorithms were used for mask wear detection, respectively using the opencv native library, MTCNN+MobileNet, and pyramidbox_lite_mobile_mask in paddlehub. Finally, the test results of the three algorithms were analyzed and compared, and the experimental results are that the pyramidbox_lite_mobile_mask model in paddlehub has the most sensitive face recognition and mask detection ability, which can identify the blurred face and judge whether to wear a mask, followed by MTCNN + MobileNet. © 2022 IEEE.

Student-initiated seminars in the undergraduate medical training program "interdisciplinary oncology" of the Medical Faculty of Heidelberg

Introduction: Longitudinal electives ("tracks") were introduced within the Heidelberg Curriculum Medicinale (HeiCuMed) in 2017. Participation in one of the 11 tracks is obligatory. Within the track "Interdisciplinary Oncology" (IO), > 150 participants can choose from > 170 courses each semester. Since students of all terms are allowed, previous knowledge and research experience are heterogeneous. Tus, medical students and participants have initiated a lecture series entitled "Insights in Research" to facilitate the entry of medical students into scientifc research. In addition, a student-led cofee meet-up ("DoktorandenCafe") was set up. Here we report on those student initiatives. Method(s): To allow medical students with a strong interest in oncology to get into contact with basic scientifc research groups at the University Hospital Heidelberg, the German Cancer Research Center (DKFZ), the Hopp Childrens' Cancer Center (KiTZ) and the National Cancer for Tumor Diseases (NCT), a seminar entitled "Insights in Research" is organized on a monthly basis by student representatives. Guest lecturers who are first or co-author of a respective paper are invited and their research work is discussed with all participants. To increase exchange between current and future medical doctoral students, the "DoktorandenCafe" was initiated by student representatives. Result(s): Since February 2020, > 70 students enrolled in the IO elective have participated in the seminars "Insights in Research" and "DoktorandenCafe". The seminar "Insights in Research" enables students to gain basic knowledge of scientifc research processes, facilitating the first contact of medical students with cancer research. Moreover, this seminar enables students to discuss scientifc topics in the field of oncology together with other participants of the course and with a researcher that actively participated in the presented research project. Additionally, the newly initiated cofee meet-up enables students who are writing or planning to write their doctoral thesis in the field of oncology to get in touch with each other and to discuss thesis-related issues. Due to the COVID-19 pandemic, both seminars were held virtually during the summer term 2020. Conclusion(s): The student-initiated seminars have a high participation rate. This indicates that student-initiated teaching initiatives should be encouraged and implemented into medical education to strengthen interest in basic and translational research.

Structures of mycobacterial ATP synthase bound to inhibitors

Tuberculosis is the second leading cause of death by infectious disease worldwide after COVID-19. Mycobacterium tuberculosis, which causes tuberculosis, is showing alarming levels of resistance to first-line antibiotics, jeopardizing efforts to eradicate the disease. Research is underway to develop new, safer drugs to treat drug resistant tuberculosis. One of these breakthrough drugs is bedaquiline, which targets the mycobacterial ATP synthase, an essential enzyme in mycobacteria. The success of bedaquiline, which has become a cornerstone of treatment for multidrug-resistant and extensively drug-resistant tuberculosis, demonstrates the importance of the mycobacterial ATP synthase as a key drug target against M. tuberculosis. Since the discovery of bedaquiline in 2005, there has been a renewed interest in developing new and improved mycobacterial ATP synthase inhibitors. We have determined electron cryomicroscopy structures of M. smegmatis ATP synthase in complex with inhibitors. We provide detailed mechanistic and structural insights into the mode of action of these compounds, which will support medicinal chemistry efforts to design new tuberculosis drugs. Our work reveals new inhibitor binding sites in the enzyme, opening the route for development of new classes of compounds and improved inhibitors. Copyright © 2022

A Systematic Review of Multimodal Approaches to Online Misinformation Detection

During the COVID-19 pandemic, the spread of pandemic-related misinformation on social media has had a significantly adverse impact on society. The sources of such misinformation usually use not only well-tailored text but also eye-catching images to establish their credibility. In this paper, we present an overview of current efforts on the task of detecting online COVID-19 conspiracy theory and misinformation. We perform a review of multimedia misinformation datasets related to the topic and an exploratory study on the state-of-the-art approaches towards these tasks. These approaches fuse textual analysis with modeling of images, propagation graphs, user reputation and fact-checking to build a comprehensive multimodal understanding of online misinformation. Our analysis indicates that using modalities in addition to text has a significant improvement on the performance of detecting misinformation, and out of the modalities presented, modeling user reputation and graph with social data are the most effective approaches. We conclude that a dataset that unifies all modalities is needed, and we present several promising directions for future research. © 2022 IEEE.

Does COVID-19 Affect Household Financial Behaviors? Fresh Evidence From China

Based on online survey data from 2020, the present study employed a logit model to examine the effects of COVID-19 on household financial behaviors in China. Additionally, the KHB (Kohler, Karlson, Holm) model was employed to explore the pathway through which COVID-19 affects household financial behaviors. These analyses revealed that household saving and borrowing behaviors were more sensitive to COVID-19 than insurance and investment behaviors. Moreover, the effects of COVID-19 on household saving and investment behaviors were found to be mediated by attitudes toward COVID-19. These findings suggest that more effective measures to reduce households' panic attitude to public health emergencies can diminish fluctuations in household financial behaviors in the short term.

Detecting COVID-19 Conspiracy Theories with Transformers and TF-IDF

The sharing of fake news and conspiracy theories on social media has wide-spread negative effects. By designing and applying different machine learning models, researchers have made progress in detecting fake news from text. However, existing research places a heavy emphasis on general, common-sense fake news, while in reality fake news often involves rapidly changing topics and domain-specific vocabulary. In this paper, we present our methods and results for three fake news detection tasks at MediaEval benchmark 2021 that specifically involve COVID-19 related topics. We experiment with a group of text-based models including Support Vector Machines, Random Forest, BERT, and RoBERTa. We find that a pre-trained transformer yields the best validation results, but a randomly initialized transformer with smart design can also be trained to reach accuracies close to that of the pre-trained transformer. Copyright 2021 for this paper by its authors.

Risk analysis of China's stock markets based on topological data structures

We choose 100 stocks from China's markets and use their daily returns from January 3, 2013 to August 31, 2020 to investigate the risk situation in China's stock markets by exploring their correlations in the sample period. We build complexes and carry out topological data analysis on them. The persistence landscapes and their LP-norms show that there are three clear turbulent periods since 2013. The dates are then detected when the stocks are strongly correlated. As is well known, the financial risks easily break out and spread in such situations, so we call the dates critical dates for risks. We can also take them as the early warning signals for potential risks. We then construct planar maximal filtered graphs on the critical dates to help discover the systematically important companies. We find that they changed obviously in three different turbulent periods. It reminds us to analyze the risks' characteristics of the risks and implement risk prevention. The method combing topological data analysis and complex networks is shown to be effective in detecting critical information from markets, and hence is worth popularizing. © 2022 The Authors. Published by Elsevier B.V.

The Prevalence and Progression of Myopia in Elementary School Students in Shanxi Province, China During the COVID-19 Pandemic.

PURPOSE: To investigate the prevalence of myopia and the risk factors associated with its progression in elementary school students during the COVID-19 pandemic in Shanxi Province, China. METHODS: The investigation included 960 students spanning first to sixth grade from six elementary schools in Shanxi Province, China. All participants received non-cycloplegic refraction and vision tests in December of 2019 (before the COVID-19 pandemic) and in June of 2020 (after classes resumed). Information concerning the students' eye-use behaviors, physical activities, diet and sleep during the pandemic was collected using a questionnaire survey. A total of 913 students (457 males) completed all tests and the questionnaire. RESULTS: The overall prevalence rate of myopia was 16.6% in December of 2019, and it increased with age. There was no gender difference in the prevalence of myopia (χ2 = 3.210, P = .073), but females exhibited a lower average spherical equivalent (SE) (P = .026). When the classes were resumed 6 months later, the overall prevalence rate of myopia was found to be 39.4%, which was significantly higher than it before the pandemic (χ2 = 117.425, P < .001). The average SE of the participants was -0.95D, which was significantly lower than the average SE (-0.43D) before the pandemic (P < .001). SE variation (ΔSE) in grade 6 was significantly higher than that in grade 1. No significant difference in ΔSE was found between males and females. Analyses of ordinary least squares (OLS)-estimated linear, natural logarithmic and quadratic functions revealed that the progression of myopia during the COVID-19 pandemic was significantly correlated with screen time, types of electronic devices, the amount of sleep, age, and the number of parents with myopia. CONCLUSIONS: The prevalence rate and progression of myopia among elementary school students in Shanxi Province increased significantly during the COVID-19 pandemic, which was likely related to China's home-based online learning programs. Therefore, it is necessary to optimize the educational programs for elementary school students when they study at home. We recommend increased time for outdoor activities and limiting screen time.

Traffic Data-Empowered XGBoost-LSTM Framework for Infectious Disease Prediction

Large-scale infectious diseases pose a tremendous risk to humans, with global outbreaks of COVID-19 causing millions of deaths and trillions of dollars in economic losses. To minimize the damage caused by large-scale infectious diseases, it is necessary to develop infectious disease prediction models to provide assistance for prevention. In this paper, we propose an XGBoost-LSTM mixed framework that predicts the spread of infectious diseases in multiple cities and regions. According to big traffic data, it was found that population flow is closely related to the spread of infectious diseases. Clustering and dividing cities according to population flow can significantly improve prediction accuracy. Meanwhile, an XGBoost is used to predict the transmission trend based on the key features of infection. An LSTM is used to predict the transmission fluctuation based on infection-related multiple time series features. The mixed model combines transmission trends and fluctuations to predict infections accurately. The proposed method is evaluated on a dataset of highly pathogenic infectious disease transmission published by Baidu and compared with other advanced methods. The results show that the model has an excellent predictive effect and practical value for large-scale infectious disease prediction.

Trajectory and Attitude Measurement of Skier based on MINS/UWB Integration for Indoor Intelligent Skiing System

The 2022 Winter Olympics bid success promoted the development of the ice and snow sports in China. The emergence of indoor skiing system drives the ski and snow sports into a highly developed period especially at the normal prevention and control stage of COVID-19. However, the conventional indoor skiing system is insufficient in sports experience and inability to track the skier trajectory and attitude for training. Fortunately, the Ultra-Wide Band (UWB) and Micro Inertial Navigation System (MINS) are widely used in indoor environments due to high-precision positioning and low-cost priorities. UWB presents high accuracy in positioning, while it is easily to be disturbed by the Non Line of Sight (NLOS) and multipath effects. Meanwhile, the MINS error would accumulate with time. Therefore, this paper proposed a MINS/UWB integration algorithm to implement the trajectory and attitude measurement of the skier with low-cost. Meanwhile, the MINS/UWB based Extended Kalman Filter (EKE) is designed with sequential algorithm for skiing. Finally, both the indoor positioning experiment and the intelligent skiing system verification experiment were carried out to verify the accuracy of MINS/UWB integration system. Experimental results show the MINS/UWB integration technology could locate effectively When the UWB signal is intermittently blocked.

Data Augmentation and CNN Classification for Automatic COVID-19 Diagnosis from CT-Scan Images on Small Dataset

We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capability of the 2D CNN classification models. We propose a unique and effective data augmentation method using multiple Hounsfield Unit (HU) normalization windows. In addition, the original slice image is cropped to exclude background, and a filter is applied to filter out closed-lung images. For the classification network, we choose to use 2D Densenet and Xception with the feature pyramid network (FPN). To further improve the classification accuracy, an ensemble of multiple CNN models and HU windows is used. On the training/validation dataset, we achieve a patient classification accuracy of 93.39%. © 2021 IEEE.

Study on the Knowledge, Attitude and Behavior of College Students in Guangxi Province, China during Covid-19 Pandemic

COVID-19 has rapidly spread through countries .This occurrence has resulted in a huge negative response from the general public;the media continuously disseminate information to keep everyone notified regarding this global pandemic state. Public education is considered one of the most important measures that might help control COVID-19. This research assessed COVID-19 knowledge, attitude, and behaviour among college students in Guangxi Province, China. A cross-sectional survey-based that was conducted online on March 16, 2020 to May 4, 2020 among College students from Guangxi Province, China. The electronic survey devised by the researchers includes four main segments relating to the profile of the participants, knowledge, attitude and behavior during the novel coronavirus pandemic. The study data revealed that the respondents had an adequate level of knowledge about the mode of transmission, symptoms and prevention strategies on COVID-19. The survey likewise illustrated an overall positive attitude and behavior toward these protective measures for the virus and its responses if an infection was contracted. Also, Majority of the students don't consider the virus as a stigma or hide it from medical specialists. They avoid the situation where they can get COVID-19 and chose to seek medical treatment or isolate themselves if needed. The current study showed enough knowledge, positive behaviour and moderate attitude towards COVID-19 among the students. This response indicates the impact of the official statement confirmed by the WHO about COVID-19 being a disease outbreak and the initiatives of the health officials to notify and educate the public regarding this pandemic. Nonetheless, most research participants displayed satisfactory responses to COVID-19 knowledge behaviour and attitudes, implying the effectiveness of the awareness campaigns. The findings of the study may prove to be the basis for preparing government programs, policies and plans to adequately manage COVID-19 and to limit its transmission. © 2021 Kriedt Enterprises Ltd. All right reserved.

Insights on In-Situ Photochemistry Associated with Ozone Reduction in Guangzhou during the COVID-19 Lockdown

Increases in ground-level ozone (O3 ) have been observed during the COVID-19 lockdown in many places around the world, primarily due to the uncoordinated emission reductions of O3 precursors. In Guangzhou, the capital of Guangdong province in South China, O3 distinctively decreased during the lockdown. Such a phenomenon was attributed to meteorological variations and weakening of local O3 formation, as indicated by chemical transport models. However, the emission-based modellings were not fully validated by observations, especially for volatile organic compounds (VOCs). In this study, we analyzed the changes of O3 and its precursors, including VOCs, from the pre-lockdown (Pre-LD) to lockdown period (LD) spanning 1 week in Guangzhou. An observation-based box model was applied to understand the evolution of in-situ photochemistry. Indeed, the ambient concentrations of O3 precursors decreased significantly in the LD. A reduction of 20.7% was identified for the total mixing ratios of VOCs, and the transportation-related species experienced the biggest declines. However, the reduction of O3 precursors would not lead to a decrease of in-situ O3 production if the meteorology did not change between the Pre-LD and LD periods. Sensitivity tests indicated that O3 formation was limited by VOCs in both periods. The lower temperature and photolysis frequencies in the LD reversed the increase of O3 that would be caused by the emission reductions otherwise. This study reiterates the fact that O3 abatement requires coordinated control strategies, even if the emissions of O3 precursors can be significantly reduced in the short term. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Role of the Built Environment in the Recovery From COVID-19: Evidence From a GIS-Based Natural Experiment on the City Blocks in Wuhan, China

The built environment closely relates to the development of COVID-19 and post-disaster recovery. Nevertheless, few studies examine its impacts on the recovery stage and corresponding urban development strategies. This study examines the built environment's role in Wuhan's recovery at the city block level through a natural experiment. We first aggregated eight built environmental characteristics (BECs) of 192 city blocks from the perspectives of density, infrastructure supply, and socioeconomic environment;then, the BECs were associated with the recovery rates at the same city blocks, based on the public "COVID-19-free" reports of about 7,100 communities over the recovery stages. The results showed that three BECs, i.e., "number of nearby designated hospitals," "green ratio," and "housing price" had significant associations with Wuhan's recovery when the strict control measures were implemented. At the first time of reporting, more significant associations were also found with "average building age," "neighborhood facility development level," and "facility management level." In contrast, no associations were found for "controlled residential land-use intensity" and "plot ratio" throughout the stages. The findings from Wuhan's recovery pinpointing evidence with implications in future smart and resilient urban development are as follows: the accessibility of hospitals should be comprehensive in general;and the average housing price of a city block can reflect its post-disaster recoverability compared to that of the other blocks.