Limits of science-based approaches in global health: Sociocultural and moral lessons from Ebola and COVID-19

This chapter explores the significance of sociocultural and ethical limitations of non-science-based approaches toward effectively containing, managing, and ending global health emergencies. It refers to the 2013-2016 Ebola epidemic in West Africa and the current COVID-19 global pandemic to underscore the limits of science-based approaches in tackling infectious disease outbreaks. Against this background, it points to the significance of measures rooted in the humanities that have been (or are being) used to demonstrate the values of social, cultural, and ethical approaches in addressing global health emergencies. This chapter shows that while science-based approaches are essential, they are not sufficient toward addressing the varied challenges of global health emergencies. The experiences of Ebola epidemics in Africa and the COVID-19 global pandemic have shown that science-based approaches need to be buttressed by sociocultural and ethical measures to be successful. It has become self-evident that global health emergencies can be addressed sooner if non-science-based approaches are incorporated into the core responses. The successful approaches toward addressing global health emergencies will be ones that adequately harmonized science-based approaches with sociocultural and ethical measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good"class. © 2023 IEEE.

Design as the Mediator of New Social Health Culture. Towards a Co-benefits Model

The COVID-19 pandemic accelerated the need for change, raising questions about the current approach to health. The re-definition of the role of health and well-being towards an interdisciplinary approach is knowledge-driven and technology-enabled and the focus of innovation is shifting from the treatment of disease to prediction and prevention. The new model of the ‘co-benefit belt' through design activates a process of systemic improvement and extends beyond the digital, pursuing the logic of interaction. The role of Design as a mediator is emphasized, lending itself to emergency situations, to the design of protection devices by implementing multifunctional and shared protection dynamics, intervening in rethinking the universe of devices with Human Centered Design approaches, optimizing methods and processes. The case study presented describes the development of the research project funded by the Campania Region, "Smart&Safe”. Design for new individual protection devices”, among the initiatives to fight against Covid-19. The research proposes an update in the redesign of individual Personal Protective Equipment (PPE), to explore a new dimension of the project that highlights the transition to an Individual and Intelligent Protection System (IIPS), reflecting on the various levels of safety faced during health emergencies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A crowd clustering prediction and captioning technique for public health emergencies.

The COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal crowd gatherings. We suggest a crowd clustering prediction and captioning technique based on a global neural network to detect and caption these scenes rapidly and effectively. We superimpose two long convolution lines for the residual structure, which may produce a broad sensing region and apply our model's fewer parameters to ensure a wide sensing region, less computation, and increased efficiency of our method. After that, we can travel to the areas where people are congregating. So, to produce news material about the present occurrence, we suggest a double-LSTM model. We train and test our upgraded crowds-gathering model using the ShanghaiTech dataset and assess our captioning model on the MSCOCO dataset. The results of the experiment demonstrate that using our strategy can significantly increase the accuracy of the crowd clustering model, as well as minimize MAE and MSE. Our model can produce competitive results for scene captioning compared to previous approaches.

Concordance between COVID-19 mortality statistics derived from clinical audit and death certificates in Östergötland county, Sweden.

OBJECTIVES: Despite early notions that correct attribution of deaths caused by SARS-CoV-2 infection is critical to the understanding of the COVID-19 pandemic, three years later, the accuracy of COVID-19 death counts is still contested. We aimed to compare official death statistics with cause-of-death assessments made in a clinical audit routine by experienced physicians having access to the full medical record. STUDY DESIGN: Health service quality evaluation. METHODS: In Östergötland county (pop. 465,000), Sweden, a clinical audit team assessed from the start of the pandemic the cause of death in individuals having deceased after testing positive for SARS-CoV-2. We estimated the concordance between official data on COVID-19 deaths and data from the clinical audit using correlations (r) between the cause-of-death categories and discrepancies between the absolute numbers of categorised deaths. RESULTS: The concordance between the data sources was poor regarding whether COVID-19 was the underlying or a contributing cause of death. Grouping of the causes increased the correlations to acceptable strength. Also including deaths implicated by a positive SARS-CoV-2 test in the clinical categorisation of COVID-19 deaths reduced the difference in absolute number of deaths; with these modifications, the concordance was acceptable before the COVID-19 vaccination program was initiated (r = 0.97; symmetric mean absolute percentage error (SMAPE) = 19%), while a difference in the absolute numbers of deaths remained in the vaccination period (r = 0.94; SMAPE = 35%). CONCLUSIONS: This study highlights that carefulness is warranted when COVID-19 death statistics are used in health service planning and resonates a need for further research on cause-of-death recording methodologies.

Forms of participation and learning of mathematics in virtual workgroups in engineering students

The objective of the research was to determine the relationship between the type of participation, collaborative organizational structure of the groups and the learning of mathematics, in a group task carried out in virtual form by the context of health emergency by COVID19. During four weeks, eight working groups composed of three environmental engineering students worked four activities on the analysis of the variation of functions. The working meetings were developed by ZOOM and WhatsApp. It was found that reasoning and argumentation as well as problem solving is favored when students express new ideas or explanations about any doubt or when they address the content of the task, especially in groups with an integrative organizational structure. © 2023 IEEE.

The High-Performance Computing Resources for the EXSCALATE4CoV Project

In order to repurpose currently available therapeutics for novel diseases, druggable targets have to be identified and matched with small molecules. In the case of a public health emergency, such as the ongoing coronavirus disease 2019 (COVID-19) pandemic, this identification needs to be accomplished quickly to support the rapid initiation of effective treatments to minimize casualties. The utilization of supercomputers, or more generally High-Performance Computing (HPC) facilities, to accelerate drug design is well established, but when the pandemic emerged in early 2020, it was necessary to activate a process of urgent computing, i.e., prioritized and immediate access to the most powerful computing resources available. Thanks to the close collaboration of the partners in the HPC activity, it was possible to rapidly deploy an urgent computing infrastructure of world-class supercomputers, massive cloud storage, efficient simulation software, and analysis tools. With this infrastructure, the project team performed very long molecular dynamics simulations and extreme-scale virtual drug screening experiments, eventually identifying molecules with potential antiviral activity. In conclusion, the EXaSCale smArt pLatform Against paThogEns for CoronaVirus (EXSCALATE4CoV) project successfully brought together Italian computing resources to help identify effective drugs to stop the spread of the SARS-CoV-2 virus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Online learning for WHO priority diseases with pandemic potential: evidence from existing courses and preparing for Disease X.

BACKGROUND: OpenWHO is the open-access learning platform of the World Health Organization (WHO) that provides online learning for health emergencies with essential health knowledge for emergencies. There is emphasis for courses on severe emerging diseases with epidemic and pandemic potential to help frontline health workers prevent, control and respond to infectious diseases. This research addresses the question of how the existing OpenWHO online courses on infectious disease were used in the countries of disease occurrence and how to prepare for disease X, a novel or unknown pathogen with pandemic potential. METHODS: OpenWHO collects self-declared demographic data from learners among which there is data on geographical location of learners. Data in infectious disease courses use on OpenWHO was collected and examined and additionally information languages used in the outbreak locations was collected. RESULTS: For most diseases in focus the online learning materials were used in countries with burden of disease. This suggests the learning material production needs to be targeted for outbreak and epidemic events. CONCLUSIONS: Findings inform the use of learning materials in disease outbreaks. Further, this use case data confirms learning providers need to add offerings in languages spoken in outbreak impacted areas.

Scale Up Multilingualism in Health Emergency Learning: Developing an Automated Transcription and Translation Tool.

World Health Organization's (WHO) emergency learning platform OpenWHO provided by Hasso Plattner Institut (HPI) delivered online learning in real-time and in multiple languages during the COVID-19 pandemic. The challenge was to move from manual transcription and translation to automated to increase the speed and quantity of materials and languages available. TransPipe tool was introduced to facilitate this task. We describe the TransPipe development, analyze its functioning and report key results achieved. TransPipe successfully connects existing services and provides a suitable workflow to create and maintain video subtitles in different languages. By the end of 2022, the tool transcribed nearly 4,700 minutes of video content and translated 1,050,700 characters of video subtitles. Automated transcription and translation have enormous potential as a public health learning tool, allowing the near-simultaneous availability of video subtitles on OpenWHO in many languages, thus improving the usability of the learning materials in multiple languages for wider audiences.

Relation between young children's anger and fathers' mental health during the covid-19 epidemic: Moderating role of gender and child number

The father-child interaction deserves attention during the COVID-19 epidemic. This study administrated the Child Anger Questionnaire and the SCL-90 Symptom Checklist to collect primary data from 1862 fathers of Chinese young children during the COVID-19 outbreak, examined the relation between young children's anger and their fathers' mental health, and verified whether the relation was moderated by the gender or the child number. The results demonstrated that the detection rate of anger among Chinese young children was 60.08%, the scores of SCL-90 factors of their fathers were significantly lower than the Chinese normal adult male norms and those of infant parents, and the anger of young children had a significant effect on their fathers' mental health. Gender and child number moderated this relation. It is of great significance to strengthen the attention to the anger of young children and the mental health of fathers during the period of public health emergencies, and to promote the harmonious interpersonal relationship between young children and their fathers. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes' Response against COVID Variants

The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error. © 2023 Tech Science Press. All rights reserved.

The COVID‐19 pandemic changes citizens' visual aesthetic perceptions and attitudes toward restored landscape in the karst area in China

Landscape change caused by ecological restoration projects in the karst rocky desertification area of southwestern China has presented ecological benefits, yet the visual aesthetic perception of the restored landscape has received less attention. Meanwhile, given the unpredictable worldwide health emergency caused by the COVID‐19 pandemic, it inspired us to be concerned about will citizens' aesthetic perceptions and attitudes to the change of restored landscape from pre‐COVID‐19 to during the outbreak of the COVID‐19 pandemic. Organizing an online survey, we explored citizens' visual aesthetic perceptions and attitudes to natural restored landscape (NRL) and managed restored landscape (MRL) on 757 citizens in Shilin Geopark (in Kunming, China), as well as how citizens' sociocultural backgrounds influence visual aesthetic preference. The results indicated that before the COVID‐19 pandemic, the professionals preferred NRL, while the nonprofessionals presented a higher preference for MRL. However, during the COVID‐19 pandemic, both two groups showed a higher preference for NRL, which implied that the experience of lockdown during the COVID‐19 pandemic might awaken most citizens' preference for the NRL. Among different kinds of restored plant communities, the landscape dominated by shrubs was the most popular. Furthermore, gender, age, career type, education, region, and citizens' visit frequency were significantly correlated with visual aesthetic perceptions before the COVID‐19 pandemic. During the COVID‐19 pandemic, professional background, gender, and age did not show significant impacts on visual aesthetic perceptions anymore. These results highlight the necessity of understanding visual aesthetic perceptions in different sociodemographic groups to encourage natural succession and create a nature‐based restored landscape in the karst area. [ FROM AUTHOR] Copyright of Restoration Ecology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Italian quick survey on the effects of the COVID-19 health emergency on businesses: Sampling strategy and data editing

During the first phase of the COVID-19 pandemic, Istat performed the quick survey "Situation and perspectives of Italian enterprises during the COVID-19 health emergency,” with the aim of assessing the economic situation and the specific actions adopted by businesses to reduce the economic impacts of the emergency. To ensure the continuity in the information flow and to analyze the temporal evolution of the observed phenomena, the survey has been repeated in three different waves. The outcomes of each wave was released just after 2 months from the launch of the survey. The present work analyses the characteristics of the sampling strategy and describes the complexity of the data editing process, in the case of a survey planned to produce estimates able to ensure an acceptable level of accuracy in the maximum timeliness. © 2023 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd.

A Large-Scale Survey on Perceived Risk, Risk Emotions and Humanistic Care Needs Among Nurses During the Covid-19 Pandemic.

Purpose: The purpose of this study is to understand the risk perception, risk emotions and humanistic care needs of nursing staff during the Novel Coronavirus 2019 (Covid-19) pandemic. Methods: A cross-sectional survey was conducted on the perceived risk, risk emotions and humanistic care needs of 35,068 nurses in 18 cities of the Henan Province, China.We collected a total of 35,188 questionnaires, of which 35,068 were effectively returned, with an effective return rate of 99.7%. The collected data were summarized and statistically analyzed using Excel 97 2003 and IBM SPSS software. Results: Nurses' risk perceptions and emotions vary during the covid-19 pandemic. In order to provide nurses with targeted psychological intervention to prevent nurses from suffering from unhealthy mental states.The results show that the total score of the nurses' risk perceptions of Covid-19 was 3.66 ± 0.39, the highest score of nurses' risk perception part is 5 points, and ≥3 points represent high risk and 88.3% of nurses believed that the Covid-19 risk was high. There were significant differences in the nurses' total perceived risk scores for Covid-19 based on gender, age, prior contact with patients with suspected or confirmed Covid-19 and previous participation in other similar public health emergencies (P < 0.050). Of the nurses included in the study, 44.8% had some level of fear relating to Covid-19 and 35.7% were able to remain calm and objective. There were significant differences in the total scores for risk emotions relating to Covid-19 based on gender, age and prior contact with patients with suspected or confirmed Covid-19 (P < 0.050). Of the nurses included in the study, 84.8% were willing to receive humanistic care and 77.6% of these expected to be provided with humanistic care by institutions in the healthcare sector. Conclusion: Nurses with different basic data have different risk cognition and risk emotions. Different psychological needs should be considered, and targeted multi-sectoral psychological intervention services should be provided to help prevent nurses from developing unhealthy psychological states.

Translation in Global Health Emergencies

The link between translation and global health is an important yet under-researched topic. COVID-19 has opened a significant responsibility and a vast space for translation scholars in approaching this topic. Starting from a brief survey of research on translation and global health, this article examines the roles of translators and interpreters in knowledge translation, and thus in the combat against COVID-19 pandemic by investigating two cases, i.e., Handbook of COVID-19 Prevention and Treatment and Jin Ji's interpreting service in Italy. It is revealed that translators and interpreters have functioned as initiators, messengers, and co-producers of COVID-19 knowledge and they, in collaboration with other actors (health practitioners, medical researchers, policymakers, etc.), have contributed to the empowerment of patients and ordinary citizens in the fight. The author argues that translation plays an indispensable part in the transcendence of frontiers (sectorial, disciplinary, cultural, and geographic) in knowledge translation, especially during global health emergencies.

Assessment of Knowledge, Reported Attitudes and Practices Regarding Emergency Preparedness and Related Factors at Tertiary-Care Hospitals of Punjab Province Amid COVID-19 Pandemic

Hospitals need to maintain a high level of preparedness of staff and systems to mitigate the consequences of health emergencies and disasters. Therefore, the knowledge, attitude and practices of the hospital staff are of key importance in strengthening the emergency preparedness of the health system.Objective: The objective of this study was to determine the knowledge, attitudes and practices of healthcare workers regarding emergency preparedness and factors related to them, at the tertiary care hospitals of Punjab PakistanMethods: This was an analytical cross-sectional study conducted at six tertiary care hospitals in Punjab from February 2022 to August 2022, approved by advanced studies and the research board of the University ofPunjab. A self-administered questionnaire was distributed among 450 staffmembers ofthese hospitals to identify gaps in the knowledge, reported attitudes and practices of healthcare workers and their willingness to report for duty, selectedby multistage sampling. Data were analysedby statistical package for social sciences (SPSS) Version 22.Results: The results found that 49.8% of the participants were aware of disasters that occurred, 50% knew the hospital emergency plan, and 70% agreed that hospitals need written plans yet 72.4% were not aware of the major components of the plan.Regarding attitude of the staff, 73.8% of accepted that it is their duty to take care of patients, 33.6% thought that hospital preparedness is adequate and only 36.7% agreed that the hospital had adequate staff in catering for the increased patient influx. Regarding hospital preparedness practices, only 29.3% stated that hospital conducts exercises and drills and 30.4% reported that the hospital conducts other training sessions and workshops for staff.Conclusion: The majority of the staff at the studied hospitals had a positive attitudes and willingness to report for duties in case of health emergencies. But there were lacks in the knowledge and practices at these hospitals which needs to be addressed by making a written hospital emergency plan, conducting simulation drills and mock exercises and arranging training.

The Early Warning Mechanism of Public Health Emergencies Through Whistleblowing: A Perspective Based on Considering the Uncertainty of Risk Perception.

Purpose: During the early warning period of public health emergencies, the information released by whistleblowers on the risk posed by the given event can reduce uncertainty in the public's risk perception and help governments take timely actions to contain the large-scale dissemination of risk. The purpose of this study is to give full play to whistleblowers and draw attention to the risk events, forming a pluralistic model of the risk governance during the early warning period of public health emergencies. Methods: We construct an evolutionary game model of the early warning of public health emergencies through whistleblowing that involves the government, whistleblowers, and the public, discussing the mechanism of interaction between these subjects under the uncertainty of risk perception. Furthermore, we use numerical simulations to analyze the influence of changes in the relevant parameters on the evolutionary trajectory of the subjects' behaviors. Results: The results of the research are obtained by numerical simulation of the evolutionary game model. The results show that the public's cooperation with the government encourages the latter to take a positive guidance strategy. Increasing the reward for whistleblowers within an acceptable cost, strengthening the propaganda of the mechanism and the higher level of risk perception of the government and whistleblowers will promote whistleblowers' vocalization actively. When the government's reward for whistleblowers is lower, the whistleblowers choose negative vocalization with the improvement of the public's risk perception. If there is no mandatory guidance from the government at this point, the public is prone to passively cooperating with the government owing to a lack of risk-related information. Conclusion: Establishing an early warning mechanism through whistleblowing is important for containing risk in the early warning period of public health emergencies. Building the whistleblowing mechanism in daily work can improve the effectiveness of the mechanism and enhance the public's risk perception better when the public health emergencies arise.

From Art Image to Video/Comic-Image Learning. The Video Spot Stay at Home

From the Covid-19 health emergency entered our lives, the web continues to alleviate moments of isolation with ironic memes, photos and videos that, despite having been considered an irreverence to the masterpieces of Art and/or one of the many uses of irony to exorcise fear, they have favored the staging of video-graphic products with a strong ‘humor' component. Within these premises, in the context of graphic design, this paper will evaluate aspects as the analysis of fashion environment as expressive language of living indoor during Covid-19 pandemic;the audiovisual languages and compositional criteria for the creation and multimedia communication of a video-graphic spot on Stay at home communication campaign. The video-graphic products were analyzed on the basis of: relationship between ‘humor' message and supporting artwork;integration between image and photo-cinematography;figurative languages generative of graphic signs;duration of audiovisual spot;sound component as key to emotional reading;communication strategies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Utilizing Sentiment Analysis to Enhance the Quality of Online Learning

The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: 'Positive,' 'Negative,' and 'Neutral.' Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique. © 2022 IEEE.

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd